Introduction of CMADS Dataset and its Application in China

Meng Xianyong, Wang Hao,Cai Si-yu, Lei Xiao-hui, Zhang Xue-song

China Institute of Water Resources and Hydropower Research

Pacific Northwest National Laboratory and University of Maryland

July 27, 2016, SWAT Conference 2016, Beijing

Who am i

Name: Meng Xian-yong

Engineer and Postdoctoral fellow, China Institute of Water Resources and

Hydropower Research (IWHR)

Education: BSc and MSc in Computer science and GIS

PhD in Atmospheric and hydrological coupling

Research: Land Surface Modeling, Model Development & Evaluation

Land–Atmosphere Interaction, Climate Modeling, Climate Change

and Impacts on Water Resources and Environment

Email: mxy0001@gmail.com

Outline

• Introduction

• Development of hydrological models and why build CMADS?

• Introduction and verification of CMADS dataset

• Verification and analysis of CMADS+SWAT mode in Heihe River Basin

• Verification and analysis of CMADS+SWAT mode in Xingjiang JingboheRiver Basin

• Summary

3

Relations between Eggs (input data), Factory (model) and cakes（output）

Input（good-

quality eggs）

MODEL/factory

Input

（bad eggs）

Output

（bad cakes）

Output

（good-

quality cakes）

• Uncertainty of hydrological process simulation mainly comes from atmospheric forcing field and hydrological models.

• Meteorological station：scarce stations, low spatial representation, deficiency and incontinuity of data series, incomplete elements (such as solar radiation).

• Model simulation: Although we can simulate continuously according to embedded equations, there are some different deviations.

Problems

Although meteorological input data is important to model output,

distribution of meteorological stations in China is uneven and scarce.

How to decrease the uncertainty of meteorology and its modes and

increase the accuracy of land surface hydrology, so as to obtain space-

time components with high accuracy and reliability?

Multivariate data assimilation is imperative。。。

Modeling Evolution

① Spatial coverage： East Asia(70-150°E, 0-60°N)；latitude-longitude grid (1280 * 960)；

② Resolution ratio： time 1h；horizontal 1/16°；

③ Time ：2009 - 2012；

④ Product category： temperature, air pressure, humidity, wind speed, radiation, precipitation driving field；

Encryption station Automatic station

Radar station

Meteorological data

Disposal methodAssimilating forcing data

ECWMF

CMADS data processing and building

Data average、recalculation、interpolation sampling、and assignment

CLDAS atmospheric forcing field

temperaturehumidity

Wind speedAir pressure

radiation

precipitation

CMADS v1.3

CMADSv1.1

CMADSv1.0

CMADS dataset

Data processing

CMADSv1.2

Hourly data averaged as daily

data

Recalculate relative

humidity by using

temperature and pressure

Bilinear interpolation

sampling

Nested assignment

sampling

The assessment of CMADS in China

（temperature, pressure, humidity and wind）

Spatial distribution of correlation coefficients between observed 3-hour

precipitation and gauged data

Constitution of CMADS

………………………………………………………………

………………………………………………………………….………………………………………………………………….

For-other-model

………………………………………………………………………………………………

Verification of CMADS+SWAT mode in Heihe River Basin in China

Land surface input data

(Digital elevation model)

(Soil distribution )

(Land use distribution)

Hydrological verification data

Atmospheric forcing input data

Figure 4 The CMADS data (annual precipitation, the highest and lowest temperature distribution) in the Heihe River Basin

5 10 15 20 25 30 35 40 45 50 55 60

-20

-10

0

10

20

月

温度

(℃)

(T1)

TWS-TMax

TWS-TMin

CMADS-TMax

CMADS-TMin

CFSR-TMax

CFSR-TMin

5 10 15 20 25 30 35 40 45 50 55 60

-20

-10

0

10

20

30

月

温度

(℃)

(T2)

TWS-TemMax

TWS-TemMin

CMADS-TemMax

CMADS-TemMin

CFSR-TemMax

CFSR-TemMin

5 10 15 20 25 30 35 40 45 50 55 60

-20

-10

0

10

20

月

温度

(℃)

(T3)

TWS-TemMax

TWS-TemMin

CMADS-TemMax

CMADS-TemMin

CFSR-TemMax

CFSR-TemMin

5 10 15 20 25 30 35 40 45 50 55 60

-20

-10

0

10

20

月

温度

(℃)

(T4)

TWS-TemMax

TWS-TemMin

CMADS-TemMax

CMADS-TemMin

CFSR-TemMax

CFSR-TemMin

Fig5 . The cumulative average monthly (from year 2009 to 2013) rainfall of TWS, CMADS and CFSR at four sites (T1-T4)

5 10 15 20 25 30 35 40 45 50 55 600

1

2

3

4

5

6

月

降水

(mm

)

(T1)

TWS-Pre

CMADS-Pre

CFSR-Pre

5 10 15 20 25 30 35 40 45 50 55 600

1

2

3

4

5

月降

水(m

m)

(T2)

TWS-Pre

CMADS-Pre

CFSR-Pre

5 10 15 20 25 30 35 40 45 50 55 600

2

4

6

8

10

月

降水

(mm

)

(T3)

TWS-Pre

CMADS-Pre

CFSR-Pre

5 10 15 20 25 30 35 40 45 50 55 600

2

4

6

8

10

月

降水

(mm

)

(T4)

TWS-Pre

CMADS-Pre

CFSR-Pre

Fig6 . The mean, maximum and minimum temperature (from year 2009 to 2013) of TWS, CMADS and CFSR at four sites (T1-T4)

Table 4 Final value and sensitive ranking of model parameters

Model assessment

Monthly-scale runoff simulation results of three modes driven by three datasets at three sub-basins

A）CMADS+SWAT mode B）TWS+SWAT mode C）CFSR+SWAT modeFigure 7 Simulation results of monthly average runoff of three different modes at Qilian Mountain control station (2009-2013))

A）CMADS+SWAT mode B）TWS+SWAT mode C）CFSR+SWAT modeFigure 8 Simulation results of monthly average runoff of three different modes at ZhaMashenke control station (2009-2013)

A）CMADS+SWAT mode B）TWS+SWAT mode C）CFSR+SWAT modeFigure 9 Simulation results of monthly average runoff of three different modes at Ying Luoxia control station (2009-2013)

Daily-scale runoff simulation results of three modes driven by three datasets at three sub-basins

A）CMADS+SWAT mode B）TWS+SWAT mode C）CFSR+SWAT modeFigure 10 Daily runoff simulation results of three different modes at Qilian Mountain control station (2009-2013)

A）CMADS+SWAT mode B）TWS+SWAT mode C）CFSR+SWAT modeFigure 11 Daily runoff simulation results of three different modes at ZhaMashenke control station (2009-2013)

A）CMADS+SWAT mode B）TWS+SWAT mode C）CFSR+SWAT modeFigure 12 Daily runoff simulation results of three different modes at Ying Luoxia control station (2009-2013)

Differences caused by water balance

Water balance chart in Heihe River Basin of three modes（TWS+SWAT, CFSR+SWAT andCMADS+SWAT）, where PREC、SURQ、LATQ、GWQ、PERCOLATE、SW、ET and WYLD represent precipitation, land surface runoff, side flow, subsurface flow, lateral seepage flow, soil water, real evaporation and runoff.

Relative elements analysis of CMADS driving SWAT model in Heihe River Basin

Figure of space-time relationships between snowmelt and soil humidity of CMADS+SWAT mode

(a) (b)

(c) (d)

(e) (f)

Analysis graph of relationships between snowmelt and soil humidity of CMADS+SWAT mode from July to August in recent five years (2009-2013)

(a) (b)

(c) (d)

(e) (f)

Verification 2: Jing-bo River Basin in Xinjiang

Digital elevation model data

Soil distribution

Soil land use distribution

Station name Latitude Longitude Station

evaluation(m)

Data

period(year)

Wenquan 44°59′ 81°02′ 1310 2009-2013

Jinghe Mountain

output

44°22′ 82°55′ 620 2009-2013

Relative information of hydrological stations in Jing-bo Biver Basin

Runoff simulation and verification based on CMADS+SWAT mode

Relative analysis between soil humidity and other variables based on CMADS+SWAT mode

Currently, CMADS can be downloaded free, welcome to use.CMADSV1.0 download link：http://westdc.westgis.ac.cn/data/6aa7fe47-a8a1-42b6-ba49-62fb33050492CMADSV1.1 download link：http://westdc.westgis.ac.cn/data/647e6569-bd21-4bea-8acc-5d38bc4cd3c0

Verification of CMADS dataset in Heihe River Basin in Qilian Mountain in China refers to:MENG Xian-yong, SHI Chun-xiang, LIU Shi-yin, WANG Hao, LEI Xiao-hui,LIUZhi-hui,JI Xiao-nan, CAI Si-yu, ZHAO Qiu-dong.CMADS datasets and its application in watershed hydrological simulation: A case study of the Heihe River Basin[J].PERRL RIVER,2016,37（7）:1-19.

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