A Distributed Biosphere-Hydrological...
1
1/1/1962 1/1/1965 1/1/1968 1/1/1971 1/1/1974 1/1/1977 1/1/1980 0 1000 2000 3000 4000 5000 6000 BIAS=-6.03% RMSE=459m 3 /s RRMSE=0.66 MSSS=0.419 Discharge (m 3 /s) Tangnaihai_Sim Tangnaihai_obv Bias = -6% RMSE = 459 m 3 /s RRMSE = 0.6 MSSS =0.419 Daily discharge comparison One dimensional model One dimensional model River Routing Scheme River Routing Scheme (Hydrotopes) Abstract A new generation hydrological model, Distributed Biosphere-Hydrological (DBH) Model, is developed to connect hydrological cycle with biosphere, climate system and human society. The vegetated surface is calculated by a realistic land surface model SiB2. The hydrological part estimates the surface runoff and calculates the interlayer exchanges and interaction between soil water and groundwater. It physically represents hydrological cycle with the support of nontraditional datasets. With the new generation model, the scope of hydrology will broaden from rainfall-runoff relationship to climatology, biosphere, ecosystem, remote sensing, and human society. The model can demonstrate the effects of natural and anthropogenic heterogeneity on hydrological cycle. The effects on hydrological responses of precipitation variability and the variability on irrigation redistributing runoff, was investigated. Runoff is underestimated if the rainfall is spatially uniformly put on large grid cell. Runoff simulation could be improved by taking into account the precipitation heterogeneity. However, the negative runoff contribution cannot be simulated by only considering the natural heterogeneity. This constructive model shortcoming can be eliminated by taking into account anthropogenic heterogeneity. The model is used to interpret the Yellow River drying up phenomenon in China. Scenarios are performed from 1960 to 2000 to quantify the effect of human activity, and to distinguish it from the effect from climate change. The results show climate change is dominated in the upper and middle reaches, and human activity is dominated in the lower reaches. The river discharge nearly half is affected by climate change and half by human activities. Climate pattern change rather than linear change contributes to the decrease of stream flow in the Yellow River. The DBH Model Strategy Manipulating Time Varying Data with the DBH Model System Until the early 1980s, hydrological models use lumped rainfall- runoff relationship to describe hydrological cycle on the land. Three generations of hydrological models have taken us from the early empirical model to where we stand now. The first, developed before the 1980s, was based on statistical relationship between rainfall and runoff, or integrated different components of the hydrological cycle in a lumped or fake- distributed way (Figure 1a). The surface parameters often were uniform over the watershed. In the 1980s, a second generation of models recognized the effects of spatial heterogeneity in the calculations of the overall hydrological behaviors (Figure 1b). At the same time, powerful computer occurred, making it possible to solve the differential equation on extremely large discretized grids. And regional, spatially varying data of land surface properties were assembled from geographical surveys. The latest new (the third) generation models used modern theories relating biosphere to provide a more realistic description of land surface hydrology (Figure 1c). The new generation models incorporate the advanced schemes to understand the response of hydrological cycle to the change of biosphere, human society and climate system. The advances of methods for translating satellite data into global surface parameter sets have driven the development of the third generation models. Developed by Qiuhong Tang A Distributed Biosphere-Hydrological Model System for Continental Scale River Basins 1 Institute of Industrial Science, The University of Tokyo; 2 Department of Hydraulic Engineering, Tsinghua University; 3 Department of Civil and Environmental Engineering, University of Washington Qiuhong Tang 1,3 , Taikan Oki 1 , Shinjiro Kanae 1 and Heping Hu 2 AGU Fall Meeting 2006 H13B-1399 Evolution of Hydrological Modeling DBH model has been developed for representing the role of both topography and land cover characteristics in hydrological cycle. The model is designed for use in a continental scale river basin and coupling with atmospheric models. Multidisciplinary developments have prompted hydrological simulation. These advances include the new insights into mass/heat flux in the Soil-Vegetation-Atmosphere Transfer (SVAT) processes, progress in getting reliable land surface information from satellite remote sensing (RS), and developments of Geographic Information System (GIS) technique to extract topographic variables from digital elevation model (DEM). DBH is developed with the merging multidisciplinary advances. The main objectives include: extraction of hydrology related information from nontraditional datasets, development of a time- continuous distributed process-based land surface hydrological model accounting for the representations of the terrains, soil, vegetation, and hydrological response, model evaluation of the new generation model, and model application to evaluate the effects of human activity and natural climate variability on hydrology cycle. A third generation model, Distributed Biosphere-Hydrological (DBH) model, is developed in this study (Tang, Q., et al., 2006a). ❶ Key Features of the DBH Model A B C D A D C B O O Vegetation condition-hydrology Climate (Energy part)-hydrology Human activity-hydrology Contributions: Biosphere (SVAT scheme) New features: DBH model is unique •Use datasets from remote sensing, in situ observation, and surveys •Can model the connections among hydrology, the rest of the earth system and human society. DBH model gives the new direction of hydrological modeling •The latest new (the third) generation models, the DBH model, used modern theories relating biosphere to provide a more realistic description of land surface hydrology. It incorporates the advanced schemes to understand the response of hydrological cycle to the change of biosphere, human society and climate system. DBH model is versatile •Can be linked to ESRI's ArcView for advanced GIS applications •Support diverse global datasets •Can simulate most of the Earth’s land with global datasets DBH model is easy to use •Links to original data rather than importing the data •Allows you to update your original data •Has automatic data and model verification routines DBH model system includes advanced module for •Manipulating time varying data •Model calibration •Mass and energy balance analysis AVHRR / LAI SiB2 Land Use Global Climate Stations 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 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 NCI values 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 22222 VALID DATA POINTS 255626 MISSING DATA POINTS 7414 R 2 = 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 CLAVR values 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 22222 VALID DATA POINTS 255626 MISSING DATA POINTS 7414 R 2 = 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. R 2 = 0.407 Cloud amount NCI values 0 2000 4000 6000 8000 10000 12000 14000 16000 22222 VALID DATA POINTS 255626 MISSING 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. R 2 = 0.572 Cloud amount SCI values 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 26000 28000 VALID DATA POINTS 255626 MISSING DATA POINTS 7414 .7-.8 .2-.3 .3-.4 .4-.5 .5-.6 .6-.7 .8-.9 .9-1. 0.-.1 .1-.2 I II III Satellite Cloud Indices and Ground Observations Tang, Q., et al., 2006b. Sellers, P.L., et al., 1996. http://hydro.iis.u-tokyo.ac.jp/DBH/ Tang, Q., et al., 2006c. The Loess Plateau, the Irrigation Districts, and the Tibetan Plateau could be precipitation, human activity, and temperature hot spots of Yellow River drying up phenomenon, respectively. Tang, Q., et al., 2006d. ❷ ❹ ❸ Performance of the DBH Model System 1983-1-1 1985-1-1 1987-1-1 1989-1-1 1991-1-1 1993-1-1 0 500 1000 1500 2000 2500 3000 3500 Discharge (m 3 /s) Tangnaihai_obv Tangnaihai_sim Monthly discharge comparison BIAS = -1.1% RMSE = 233 m 3 /s RRMSE = 0.3 MSSS =0.828 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 0 500 1000 1500 2000 2500 3000 3500 4000 BIAS=-6.1% RMSE=333m 3 /s RRMSE=0.48 MSSS=0.646 Discharge (m 3 /s) Tangnaihai_sim Tangnaihai_obv Monthly discharge comparison 1983-1-1 1985-1-1 1987-1-1 1989-1-1 1991-1-1 1993-1-1 0 1000 2000 3000 4000 Discharge (m 3 /s) Tangnaihai_obv Tangnaihai_sim Daily discharge comparison Bias = -1.1% RMSE = 297 m 3 /s RRMSE = 0.4 MSSS =0.759 Murphy, 1988. MSSS (mean square skill score) 1955 1960 1965 1970 1975 0 5 10 15 20 25 30 35 40 Water withdrawals (109m3) UP_rep. UP MID_rep. MID LOW_rep. LOW TOT_rep. TOT 1980 1985 1990 1995 2000 0 5 10 15 20 25 30 35 40 Water withdrawals (109m3) UP_rep. UP MID_rep. MID LOW_rep. LOW TOT_rep. TOT Simulated and reported irrigation water withdrawals The Yellow River Basin Simulation: Spatial: 10*10 km; Time step: hourly; Calibration (1983-1993) Validation (1962-1982) Applications of the DBH Model System Effects of human activities on water components 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 AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN -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 MIN AVG 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) TNH LZ QTX TDG LM SMX HYK 0 500 1000 1500 2000 2500 Discharge along river (m 3 /s) Observed Case 1 Case 2 Case 3 decreasing discharge discharge increases (RAZ) 41% 59% Effects of natural and anthropogenic heterogeneity Case 1: none Case 2: natural heterogeneity Case 3: natural and anthropogenic -- -- O Scenario 6 O No pattern and no linear change -- No linear change / With change -- -- -- Scenario 5 -- / / Scenario 4 / -- / Scenario 3 / / -- Scenario 2 / / / Scenario 1 Irrigated Area (I) Vegetation (V) Climate (C) Scenarios 1950-1959 1960-1969 1970-1979 1980-1989 1990-1995 0 50 100 150 200 250 Irrigated area (10hm) Y Upstream Midstream Downstream Upstream_no change Midstream_no change Downstream_no change Scenarios With(out) C change With(out) V change With(out) I change 1) Climate change (75%) is dominated in upper/middle reaches, and human activity is dominated in lower reaches. 2) Climate pattern change (30%) rather than linear change (10%) is more important for the Yellow River drying up phenomenon. http://hydro.iis.u-tokyo.ac.jp/DBH/ Welcome to Homepage With Source code Documentations Tang, Q., et al., 2006e. ❺ References 1) Tang, Q., 2006a. A Distributed Biosphere-Hydrological Model for Continental Scale River Basins. The University of Tokyo, Tokyo, Japan, Ph.D. thesis. 2) Tang, Q., Oki, T., Hu, H., 2006b. A distributed biosphere hydrological model (DBHM) for large river basin. Ann. J. Hydraul. Eng. JSCE 50, 37-42. 3) Sellers, P. J., et al., 1996. A revised land surface parameterization (SiB2) for atmospheric GCMs - Part I: model formulation. J. Climate 9, 676-705. 4) Tang, Q., Oki, T., 2006c. Daily NDVI relationship to cloud cover. J. Appl. Meteorol., accepted. 5) Tang, Q., Oki, T., Kanae, S., Hu, H., 2006d. A spatial analysis of hydro-climatic and vegetation condition trends in the Yellow River Basin. Hydrol. Process., accepted. 6) Murphy, A. H., 1988. Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Weather Rev. 16, 2417-2424. 7) Tang, Q., Oki, T., Kanae, S., Hu, H., 2006e. The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. J. Hydromet., accepted.
Transcript of A Distributed Biosphere-Hydrological...
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
One dimensional modelOne dimensional model
River Routing SchemeRiver Routing Scheme
(Hydrotopes)
Abstract
A new generation hydrological model, Distributed Biosphere-Hydrological (DBH) Model, is developed to connect hydrological cycle with biosphere, climate system and human society. The vegetated surface is calculated by a realistic land surface model SiB2. The hydrological part estimates the surface runoff and calculates the interlayer exchanges and interaction between soil water and groundwater. It physically represents hydrological cycle with the support of nontraditional datasets. With the new generation model, the scope of hydrology will broaden from rainfall-runoff relationship to climatology, biosphere, ecosystem, remote sensing, and human society.
The model can demonstrate the effects of natural and anthropogenic heterogeneity on hydrological cycle. The effects on hydrological responses of precipitation variability and the variability on irrigation redistributing runoff, was investigated. Runoff is underestimated if the rainfall is spatially uniformly put on large grid cell. Runoff simulation could be improved by taking into account the precipitation heterogeneity. However, the negative runoff contribution cannot be simulated by only considering the natural heterogeneity. This constructive model shortcoming can be eliminated by taking into account anthropogenic heterogeneity.
The model is used to interpret the Yellow River drying up phenomenon in China. Scenarios are performed from 1960 to 2000 to quantify the effect of human activity, and to distinguish it from the effect from climate change. The results show climate change is dominated in the upper and middle reaches, and human activity is dominated in the lower reaches. The river discharge nearly half is affected by climate change and half by human activities. Climate pattern change rather than linear change contributes to the decrease of stream flow in the Yellow River.
The DBH Model Strategy Manipulating Time Varying Data with the DBH Model System
Until the early 1980s, hydrological models use lumped rainfall-runoff relationship to describe hydrological cycle on the land. Three generations of hydrological models have taken us from the early empirical model to where we stand now.
The first, developed before the 1980s, was based on statistical relationship between rainfall and runoff, or integrated different components of the hydrological cycle in a lumped or fake-distributed way (Figure 1a). The surface parameters often were uniform over the watershed.
In the 1980s, a second generation of models recognized the effects of spatial heterogeneity in the calculations of the overall hydrological behaviors (Figure 1b). At the same time, powerful computer occurred, making it possible to solve the differential equation on extremely large discretized grids. And regional, spatially varying data of land surface properties were assembledfrom geographical surveys.
The latest new (the third) generation models used modern theories relating biosphere to provide a more realistic description of land surface hydrology (Figure 1c). The new generation modelsincorporate the advanced schemes to understand the response of hydrological cycle to the change of biosphere, human society andclimate system. The advances of methods for translating satellite data into global surface parameter sets have driven the development of the third generation models.
Developed by Qiuhong Tang
A Distributed Biosphere-Hydrological Model System for Continental Scale River Basins
1Institute of Industrial Science, The University of Tokyo; 2Department of Hydraulic Engineering, Tsinghua University; 3Department of Civil and Environmental Engineering, University of Washington
Qiuhong Tang1,3, Taikan Oki1, Shinjiro Kanae1 and Heping Hu2
AGU Fall Meeting 2006 H13B-1399
Evolution of Hydrological Modeling
DBH model has been developed for representing the role of both topography and land cover characteristics in hydrological cycle. The model is designed for use in a continental scale river basin and coupling with atmospheric models.
Multidisciplinary developments have prompted hydrological simulation. These advances include the new insights into mass/heat flux in the Soil-Vegetation-Atmosphere Transfer (SVAT) processes, progress in getting reliable land surface information from satellite remote sensing (RS), and developments of Geographic Information System (GIS) technique to extract topographic variables from digital elevation model (DEM). DBH is developed with the merging multidisciplinary advances. The main objectives include: extraction of hydrology related information from nontraditional datasets, development of a time-continuous distributed process-based land surface hydrological model accounting for the representations of the terrains, soil, vegetation, and hydrological response, model evaluation of the new generation model, and model application to evaluate the effects of human activity and natural climate variability on hydrology cycle.
A third generation model, Distributed Biosphere-Hydrological (DBH) model, is developed in this study (Tang, Q., et al., 2006a).
❶
Key Features of the DBH Model
A B C D
A
D
CBO
O
Vegetation condition-hydrology
Climate (Energy part)-hydrology
Human activity-hydrology
Contributions:
Biosphere (SVAT scheme)
New features:
DBH model is unique•Use datasets from remote sensing, in situ observation, and surveys•Can model the connections among hydrology, the rest of the earth system and human society.
DBH model gives the new direction of hydrological modeling•The latest new (the third) generation models, the DBH model, used modern theories relating biosphere to provide a more realistic description of land surface hydrology. It incorporates the advanced schemes to understand the response of hydrological cycle to the change of biosphere, human society and climate system.
DBH model is versatile•Can be linked to ESRI's ArcView for advanced GIS applications•Support diverse global datasets•Can simulate most of the Earth’s land with global datasets
DBH model is easy to use•Links to original data rather than importing the data•Allows you to update your original data•Has automatic data and model verification routines
DBH model system includes advanced module for•Manipulating time varying data •Model calibration •Mass and energy balance analysis
AV
HR
R / L
AI
SiB
2 Lan
d U
se
Glob
al Clim
ate 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
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 val
ues
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 val
ues
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
I
II
III
Satellite Cloud Indices and Ground Observations
Tang, Q., et al., 2006b. Sellers, P.L., et al., 1996. http://hydro.iis.u-tokyo.ac.jp/DBH/
Tang, Q., et al., 2006c.
The Loess Plateau, the Irrigation Districts, and the Tibetan Plateau could be precipitation, human activity, and temperature hot spots of Yellow River drying up phenomenon, respectively.
Tang, Q., et al., 2006d.
❷
❹❸
Performance of the DBH Model System
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
Monthly discharge comparisonBIAS = -1.1% RMSE = 233 m3/s RRMSE = 0.3 MSSS =0.828
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
Monthly discharge comparison
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
Murphy, 1988. MSSS (mean square skill score)
1955 1960 1965 1970 19750
5
10
15
20
25
30
35
40
Wa
ter
with
dra
wa
ls (
109m
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
Wa
ter
with
dra
wa
ls (
109m
3)
UP_rep. UP MID_rep. MID LOW_rep. LOW TOT_rep. TOT
Simulated and reported irrigation water withdrawals
The Yellow River Basin
Simulation:Spatial: 10*10 km; Time step: hourly;
Calibration (1983-1993) Validation (1962-1982)
Applications of the DBH Model SystemEffects of human activities on water components
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
AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN
-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 Irrigation Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN
Mean energy components in peak irrigation month (JJA, 1983-2000)
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
decreasing discharge
discharge increases
(RAZ)
41%
59%
Effects of natural and anthropogenic heterogeneity
Case 1: noneCase 2: natural heterogeneityCase 3: natural and anthropogenic
----OScenario 6
O No pattern and no linear change-- No linear change/ With change
------Scenario 5
--//Scenario 4
/--/Scenario 3
//--Scenario 2
///Scenario 1
Irrigated Area (I)Vegetation (V)Climate (C)Scenarios1950-1959 1960-1969 1970-1979 1980-1989 1990-1995
0
50
100
150
200
250
Irriga
ted
area
(104 h
m2)
Y
Upstream Midstream Downstream Upstream_no change Midstream_no change Downstream_no change
Scenarios
With(out) C changeWith(out) V changeWith(out) I change
1) Climate change (75%) is dominated in upper/middle reaches, and human activity is dominated in lower reaches.
2) Climate pattern change (30%) rather than linear change (10%) is more important for the Yellow River drying up phenomenon.
http://hydro.iis.u-tokyo.ac.jp/DBH/
Welcome to
Homepage
With
Source code
Documentations
Tang, Q., et al., 2006e.
❺ References 1) Tang, Q., 2006a. A Distributed Biosphere-Hydrological Model for Continental Scale River Basins. The University of Tokyo, Tokyo, Japan, Ph.D. thesis. 2) Tang, Q., Oki, T., Hu, H., 2006b. A distributed biosphere hydrological model (DBHM) for large river basin. Ann. J. Hydraul. Eng. JSCE 50, 37-42. 3) Sellers, P. J., et al., 1996. A revised land surface parameterization (SiB2) for atmospheric GCMs - Part I: model formulation. J. Climate 9, 676-705. 4) Tang, Q., Oki, T., 2006c. Daily NDVI relationship to cloud cover. J. Appl. Meteorol., accepted. 5) Tang, Q., Oki, T., Kanae, S., Hu, H., 2006d. A spatial analysis of hydro-climatic and vegetation condition trends in the Yellow River Basin. Hydrol. Process., accepted. 6) Murphy, A. H., 1988. Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Weather Rev. 16, 2417-2424. 7) Tang, Q., Oki, T., Kanae, S., Hu, H., 2006e. The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. J. Hydromet., accepted.
