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How to maximize the predictive value of available data in ungauged basins?
Suxia Liu1,*, Changming Liu1, Weimin Zhao2
1. Key Laboratory of Water Cycle & Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;2. The Bureau of Hydrology, Yellow River Conservancy Committee, Zhengzhou 450004, China
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Suxia Liu
Lake Donghu, Hubei
Luancheng, North China Plain
SonghuangRiver, NorthEastChina
Yanghe, Hebei
GouburnRiver,NSW,Australia
Dawen River, Laiwu Shandong
Akesu River,Xinjiang
Niqu River, Sichuan
Wuding River, Shaanxi
Liangxi River, Wuxi, Jiangsu
Lushi River, Henan
NiquRiver,Sichuan
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ϖStrengthen the hydrological theory
ϖExtend the prediction of discharge to all the hydrological elements
ϖ Not only on the predictions of elements, but also on the Responses and among others
ϖ Not only hydrological, but also Interdisciplinary
My research interests¬ Interface Processes¬ Spatial and Temporal Variation of Hydrological elements
¬ Hydrological Response to CC and LUCC
¬ Environmental Flow
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• Established in 2004• Scientific Chair: Professor Academician
Changming Liu• Chair: Professor Jun Xia• Vice Chair: Professor Dawen Yang, etc• Secretary General: Professor Suxia Liu
China PUB Working Group
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IAHS-PUB-CHINA meeting2004
Activities of China PUB so far
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PUB Seminar 2004
Activities of China PUB so far
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IAHS-PUB-CHINA 2006
Activities of China PUB so far
IAHS Publication No. 322 (RED book)
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IAHS-PUB-CHINA 2008
Activities of China PUB so far
IAHS Publication No. 335 (RED book)
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IAHS-PUB-CHINA 2010
Activities of China PUB so far
Special issue……..
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• If there is no food at home, how will a house wife maximize the nutrition value of available food under the no-rice situations?
How?to maximize the predictive value of available data in ungaugedbasins?
Liu et al. Advances in Geographical Science, 2010
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• She will– Borrow (maps for interpolating&transplanting)– Replace----to find some substitutes
• From the same region, (by modeling, rational; innovation ideas)
• From other regions, (comparative analysis, paired-catchment and upscaling )
– Generate (field and laboratory experiment)
How?to maximize the predictive value of available data in ungauged basins?
Liu et al. Advances in Geographical Science, 2010
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• Maps, annals of hydrological elements for interpolating and transplanting data from known regions
(1) By borrowingHow?to maximize the predictive value of available data in ungauged basins?
Data A Neighbors’ data Aborrow
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sdinfo1.chinawater.net.cn/yytj/Wr_narco.htm
How?to maximize the predictive value of available data in ungauged basins?
Regional map
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• Simple, but useful, usually for estimating annual runoff.
• Should and can be extended to all elements including evapotranspiration, soil moisture, etc.
• Hard to be sure in mountain areas,needs better interpolation method.
• GIS will enhance its applications
How?to maximize the predictive value of available data in ungauged basinsBy borrowing
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• By using hydrological models
(2.1)Replace _from the same regionHow?to maximize the predictive value of available data in ungauged basins
Input data A
model
Prediction and estimation B
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• Simple (conceptual model, rational formula,etc), or complex model (process-based model, eco-hydrological model, etc)?
Both have pro and cons but each plays important roles in different ways for PUB
How?to maximize the predictive value of available data in ungauged basinsBy Replacing from the same region
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• Example By using HIMS model.
(2.1)Replace _from the same regionHow?to maximize the predictive value of available data in ungauged basins
Input data A
model
Prediction and estimation B
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Framework of HIMSFramework of HIMS
HydroInformatic Modeling System (HIMS)
Database
♠Module based♠Model Customized
Indoor Exp.
♠Distributed Models♠Lumped Models
Water Resources Management
Mechanism
StructureO
bjects
Field ObservationHydrologic Processes
Model-baseGIS/RS
Pollution and Erosion Climate ChangeFlood
Forecasting
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Components of HIMS:
HydroInformatic System Based on GIS
With the component-based GIS software (SuperMap),
HIMS is able to manage the hydrologic database efficiently
and derivate a watershed effectively.数字流域信息提取系统
D EM分析 图形输出
新增流域
删除流域
坡度坡向
流 向
水流累积
等流时线
河 网
子 流 域
文 件
编号 ~坐标
提取子D EM
打开文件
设 置
退 出
数据 /图形
D EM图
坡度图
坡向图
流向图
地形指数图
水流累积图
河网图
子流域图
帮 助
操作说明
关 于
隐藏属性
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Case study of the daily scale model in the Jinghe River Basin
Spatial patterns of runoff and runoff coefficientSpatial patterns of runoff and runoff coefficient
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Case study of Daily model in 331 watersheds of Australia
Precipitation <400mm 400-600mm 600-800mm 800-
1200mm >1200mm mean
Area< 100 km2 - 0.60 0.60 0.67 0.79 0.66
100-500 km2 0.56 0.51 0.78 0.78 0.84 0.73
500-1000 km2 - 0.52 0.70 0.81 0.69 0.68
>1000 km2 - 0.25 0.80 0.81 0.75 0.65
Mean 0.56 0.47 0.72 0.76 0.77 0.68
0
2000
4000
6000
8000
1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001
效率系数:0.67
020040060080010001200
1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401 9001
实测SI MHYDHI MS
效率系数:0.51
0
2000
4000
6000
8000
10000
1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001
效率系数: 0.84
•Dataset: 331 watersheds, 50 years daily precipitation and potential evaporation;
•Efficiency Coefficient: average to 0.68
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¬Flood events prediction
♠ DEM resolution:30-100m
♠ Temporal resolution: minutes to hours
Efficiency Coefficient>0.8
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The HIMS model, with good modeling behavior in different climate zone, shows high potential for regionalization,the potential to be used in ungauged basins.
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• Example by using VIP model.
(2.1)Replace _from the same regionHow?to maximize the predictive value of available data in ungauged basins
Input data A
model
Prediction and estimation B
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VIP model
Mo et al. AEE, 2009
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• Three sources ( sunlit and shaded canopy and soil surface) energy balance
• Variable storage runoff formation curve • Vegetation ecological dynamic
parameterization
Features
Mo et al, AFM,2001
Mo et al. MAP, 2004
Mo et al. EM, 2005
Mo et al. AEE, 2009
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Wuding River
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Model Output
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0
600
1200
1800
1988 1989 1990 1991 1992 1993Year
Dis
char
ge(m
3 s-1)
modelledObserved
0
600
1200
1800
1993 1994 1995 1996 1997 1998Year
Dis
char
ge(m
3 s-1) modelled
Observed
-2
1
4
7
-2 1 4 7Modelled log(Q)
Obs
erve
d lo
g(Q
)
Model Validation
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-40
-20
0
20
40
Cao
ping
Lijia
he
Qin
gyan
gca
Sui
de
Hen
gsha
n
Dijia
gou
Bai
jiach
uan
[%]
Y:(Q_Sim-Q_obs)/Q_obs
Model Validation
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Annual scale
Model Validation
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Model Validation
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Suide
020406080
100
92-1-8
92-7-8
93-1-8
93-7-8
94-1-8
94-7-8
95-1-8
95-7-8
96-1-8
96-7-8
97-1-8
97-7-8
Rel
ativ
e SM
(%)
VIP TUW OBS
Yulin
020406080
100
1992-1-8
1992-7-8
1993-1-8
1993-7-8
1994-1-8
1994-7-8
1995-1-8
1995-7-8
1996-1-8
1996-7-8
1997-1-8
1997-7-8
Rel
ativ
e S
M (%
)Model Validation
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01020304050607080
J F M A M J J A S O N DMon
Rel
ativ
e SM
(%)
tuw
VIP_normalized
Model Validation
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North Drying,
Model applications----generate long-term data series for future trend
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SM decrease at 0.01;
Discharge decrease at 0.001;
P decrease at 0.1,
T increases at 0.01
Model applications----generate long-term data series for future trend
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Model applications----explore the response of hydrological elements to LUCC
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dT, dP
C L Q H S D B+0.5k, +10% 0.19 0.15 0.13 0.16 0.15 0.18 0.19
+0.5k, -10% -0.20 -0.15 -0.13 -0.16 -0.17 -0.17 -0.20+1.0K,+10% 0.18 0.12 0.11 0.16 0.13 0.16 0.16+1.0K, -10% -0.21 -0.17 -0.14 -0.16 -0.18 -0.18 -0.22• Response exists spatial variability。
• When p increases, the response will be larger with the temperature gain
• When p decreases, the response will be smaller with the temperature gain
• Response to precipitation is proportional
Model applications----explore the response of hydrological elements to climate change
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1km Rule,
Mo X, Liu S, et al, HSJ, 2009
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1km Rule,
Mo X, Liu S, et al, HSJ, 2009
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Uncertainty
Mo X, Liu S, et al, HSJ, 2006
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The VIP model, although in very detail, but no need for calibration,can catch the mechanisms of the response for ungauged basin, which is useful to provide scientific basis for decision makers for water-saving agriculture, making reasonalbeadaptation measures to climate change and land-use/cover change.(explain why, tell how)
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• By using rational formula (model)
(2.1)Replace _from the same regionHow?to maximize the predictive value of available data in ungauged basins
Input data A
model
Prediction and estimation B
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21 95.0 kkx +=
rtFfP )1(1
τ−===
τtP =1
3.035.011
15.033.0
22
5.05.02 278.0278.0
mm QIAL
QIAFL
+=τ 3.01
5.02
−− += mm QkQkτ
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Liu and Wang,Water Resources Research,16(5), 1980
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The above rational formulae have been successfully used in designing the culvert and bridge construction along the 8 important railways in western ungauged area of China.
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• Example by using innovational ideas(model)
(2.1)Replace _from the same regionHow?to maximize the predictive value of available data in ungauged basins
Input data A
model
Prediction and estimation B
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Environmental Flow
Eco-hydraulic Radius method, Liu et al. 2008, Progress in Natural Sciences
LIHAFLOVA method, Liu et al. 2008, NSDBYSLKJ,
Relation between Amphibians and flow regimes, Liu et al. GWSP newsletter, 2008,
Analytical Wetted Perimeter method, Liu et al.Journal of Geographical Sciences, 2006,
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The above new methods were successfully used in western ungauged area of China for decisions-makers for planning the project to transfer water from South to North.
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• Comparative analysis
• or
(2.2)Replace_from the other regionHow?to maximize the predictive value of available data in ungauged basins
A B
AB
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Comparative Hydrology (CH)• Brankov (1946)• Chapman, T, G, Philosophy and analytical
approaches, Section 1, Comparative Hydrology, draft 2, June 1986
• 1986,IHP_III, CH workshop• M. Falkenmark and T. Chapman, Editors,
Comparative hydrology. An ecological approach to land and water resources, UNESCO, Paris (1989).
• Ming-Ko Woo, Changming Liu, Mountain hydrology of Canada and China: A case study in comparative hydrology Hydrological Processes,8(6)573-587,1994
• FRIEND-UNESCO, 1999-2000
0
0
PPR
R xx =
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Comparative Hydrology (CH)• 。。。。。。。• McDonnell, J.J. and R. Woods (2004)
Editorial: On the need for catchmentclassification. Journal of Hydrology, 299 (1-2), 2-3.…
• G. Blöschl, R. Merz, Thoughts on a process-based catchment classification,Geophysical Research Abstracts,Vol. 10, EGU2008-A-08153, 2008
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Significance in Comparative Hydrology is to provide a base for understanding the dissimilarity and similarity in runoff formation of gauged and ungauged basins that is needed for deduction of the hydrological processes.
Basic law in nature
Deduction
Non-Deduction
Hydrological processes in different climate zones
Technology Data Model Direct Comparison based on observed data
Transfer between the climate zones
Basic law in nature
Deduction
Non-Deduction
Hydrological processes in different climate zones
Technology Data Model Direct Comparison based on observed data
Transfer between the climate zones
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paired-catchment analysis
Liu et al. MAP, 2004
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• Field and laboratory experiments, remote sensing
(3)GenerateHow?to maximize the predictive value of available data in ungauged basins
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Field survey and artificial rainfall-runoff experiments
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Field survey and artificial rainfall-runoff experiments over many different basins.
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Remote Sensing
VIP
model
In-situ
Long-term soil moisture data
大气驱动 (温度, 降雨, 辐射, 气压, 风, CO2浓度。。。 )植被管理 ( 灌溉, 施肥)
枯枝落叶
土层 1
土层 n
:
土壤水
水循环
蒸发(潜热)
能量平衡 碳循环
氮循环
植被动力学/作物生长
NPP,NEP
深层排泄
径流
光合作用/气孔导度
土壤热
CO
2
氮
冠层截留水
下渗
辐射吸收、转换
显热
大气驱动 (温度, 降雨, 辐射, 气压, 风, CO2浓度。。。 )植被管理 ( 灌溉, 施肥)
枯枝落叶
土层 1
土层 n
:
土壤水
水循环
蒸发(潜热)
能量平衡 碳循环
氮循环
植被动力学/作物生长
NPP,NEP
深层排泄
径流
光合作用/气孔导度
土壤热
CO
2
氮
冠层截留水
下渗
辐射吸收、转换
显热
Liu et al. HESS, 2009
Data Assimilation
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¬Strengthen PUB theoretical study: now more in skill; theory in regional hydrology, comparative hydrology, PUB theoretical framework, in-situexperiments and general models, are highly wanted.
Some more to be done in the near future
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¬ Aim for innovative methods: Now all the methods are still in the general framework of the methodology
Some more to be done in the near future
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¬Find more applications of PUB. Now more in theoretical study, few on the practical problem solving
Some more to be done in the near future
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How to maximize the predictive value of available data in ungauged basins?
BySuxia Liu, Changming Liu and Weimin Zhao