landscape ecology and ecosystem science (lees) lab department of environmental sciences

Landscape Ecology and Ecosystem Science (LEES) LabDepartment of Environmental Sciences

The University of Toledo

Regional Climate Change and Vegetation Regional Climate Change and Vegetation Water Water Relations in Inner MongoliaRelations in Inner Mongolia

Lessons Learned within “Effects of Land Use Change on the Energy and Water Balance of the Semi-Arid Region of Inner Mongolia, China” NASA’s NEESPI

Nan Lu

IntroductionIntroduction

The global climate has changed rapidly in the past century with the global mean temperature increased by 0.7 C (IPCC, 2007).

Studies on how climate change drives changes in ecosystem processes (carbon, water, energy cycles, etc.) and their feedbacks are the current scientific frontiers (Lucier et al., 2006).

It requires multiple techniques and analyses to understand these scale-dependent interactions and provide scientific foundation to policymakers.

The Northern Eurasia Earth Science Partnership Initiative (NEESPI), has been initiated by the NASA Land Use and Land Cover Change program (LULCC) to understand the feedbacks between climate, land surface processes and anthropogenic activities in Eurasia at latitudes > 40N.

Research Context of My Research Context of My StudiesStudies

LEES Lab focused on “The Effects of Climate and Land Use Change on the Energy and Water

Balance of the Semi-Arid Inner Mongolia”.

37°01’ - 53°02’ N 95°02‘ - 123°37' E

Study Region: Inner Mongolia Study Region: Inner Mongolia (IM)(IM)

Area: 1.18 million km2

Elevation: 86 - 3352 mAnnual mean air temperature: 4˚C Annual precipitation: 308 mm

Olson et al., 2001, John et al., 2008

2.9˚C, 450mm

2.6˚C, 350mm

6.8˚C, 200mm

Sub-humid

Semi-arid

Arid

IM has experienced a significant land cover change during the last decades , due to climate change and anthropogenic influences related to population increase and socio-economic development (Zhang 1992; John et al. submitted).

However, the changes in hydrological and energy processes in this regions under the frame of climate and land cover changes have not been well studied.

Land Use EffectsClimate Effects

SiB3

com

pa

riso

n

time

Regional Database

energy

comparison

MODIS(VI, albedo, T)

regional RS modeling

stable isotopepartitioning

Land Cover

landscape

Landsat ETM+

spa

ti al

pa

r am

et e

riza

t ion

scenarios

Tower(ET, Rn, G)

Tower 3.1 (less disturbed)

Tower 3.2 (intensively disturbed)





Mobile EC Tower ecosystem 1 in FY1ecosystem 2 in FY2ecosystem 3 in FY3

landscape 3landscape 1 landscape 23 ecosystemslandscape 1-3

Energy Mobile Towerlandscape 1 in FY 1landscape 2 in FY 2landscape 3 in FY 3

VegetationSoil

Climate

Public Web Acess

com

pa

r iso

n

supervisedclassification

QA/QC

Task 4

Task 3

Task 4

Task 1

Task 2

scenariosE, Tr, EF

water

* Study 1, 2

* Study 3

* Study 4

My research as part of the proposed activities to quantify the water and energy cycles in IM (within the LEES-NASA project).

Rationale of My Rationale of My DissertationDissertation

To evaluate the long-term change of climate at regional, biome and local scales.

To examine the spatial and temporal variability of climate extremes and the dependency on biogeographical features.

To examine the dynamics of the major components of water balance and their interactions in three paired ecosystems.

To evaluate the soil moisture-vegetation relationship at a large scale and develop empirical models for soil moisture downscaling.

Chapter 2: Study One

Chapter 3: Study Two

Chapter 4: Study Three

Chapter 5: Study Four

Meteorological Records

Spatial Interpolation

Eddy-Covariance

(EC)

Remote Sensing Products

Climate Change in Inner Mongolia Climate Change in Inner Mongolia from 1955 to 2005 from 1955 to 2005

– – Trends at Regional, Biome and Trends at Regional, Biome and Local ScalesLocal Scales

Study One

Introduction to Study OneIntroduction to Study One

The rates of climate change are usually different among regions due to the varied land surface properties interacting with the climate in different ways (Meissner et al., 2003; Snyder et al., 2004; Dang et

al., 2007).

IM divides into three biomes: forest, grassland and desert (Olson et

al., 2001), and each biome has different natural and anthropogenic ecology.

However, how the climate change varies among the biomes in IM has not been investigated.

ObjectiveObjective

To examine the climate changes over the past 50 years ( i.e., 1955-2005) at regional, biome and local scales, with a particular focus on the differences among the biomes.

Climatic variables: Mean, max, min air temperature (Tmean, Tmax, Tmin) Diurnal temperature range (DTR) Vapor pressure deficit (VPD) Precipitation (PPT)

Data source: 51 meteorological stations China Meteorological Data Sharing Service System

Data analysis: Least square linear regression to examine the long-term trends T-test with repeated procedure (i.e., year repeated) to examine the differences between decadal means

MethodsMethods

(10) (23)(18)

Regional Climate Change and the Regional Climate Change and the Variations among the BiomesVariations among the Biomes

Year

Tmean Tmin DTR VPD PPT

Reg

ion

Fo

rest

Gra

ssla

nd

Des

ert

a) 0.35˚ C/10yr *

0

2

4

6

8

1955 1965 1975 1985 1995 2005

c) -0.26˚ C/10yr *

11

12

13

14

15

1955 1965 1975 1985 1995 2005

B

f) 0.20˚ C/10yr *

-2

0

2

4

6

1955 1965 1975 1985 1995 2005

A

k) 0.39˚ C/10yr *

-2

0

2

4

6

1955 1965 1975 1985 1995 2005

A

p) 0.42˚ C/10yr *

2

4

6

8

10

1955 1965 1975 1985 1995 2005

C

h) -0.07˚ C/10yr

11

12

13

14

15

1955 1965 1975 1985 1995 2005

B

m) -0.26˚ C/10yr *

11

12

13

14

15

1955 1965 1975 1985 1995 2005

A

r) -0.39˚ C/10yr *

12

13

14

15

16

1955 1965 1975 1985 1995 2005

b) 0.48˚ C/10yr *

-6

-4

-2

0

2

1955 1965 1975 1985 1995 2005

B

g) 0.23˚ C/10yr *

-8

-6

-4

-2

0

1955 1965 1975 1985 1995 2005

A

l) 0.52˚ C/10yr *

-8

-6

-4

-2

0

1955 1965 1975 1985 1995 2005

A

q) 0.59˚ C/10yr *

-4

-2

0

2

4

1955 1965 1975 1985 1995 2005

d) 0.02kPa/10yr *

0.4

0.5

0.6

0.7

0.8

1955 1965 1975 1985 1995 2005

C

i) 0.01kPa/10yr *

0.3

0.4

0.5

0.6

0.7

1955 1965 1975 1985 1995 2005

B

n) 0.02kPa/10yr *

0.3

0.4

0.5

0.6

0.7

1955 1965 1975 1985 1995 2005

A

s) 0.03kPa/10yr *

0.6

0.7

0.8

0.9

1

1955 1965 1975 1985 1995 2005

e) -4.3mm/10yr

100

200

300

400

500

600

1955 1965 1975 1985 1995 2005j) -2.3mm/10yr

300

400

500

600

700

800

1955 1965 1975 1985 1995 2005o) -5.4mm/10yr

100

200

300

400

500

600

1955 1965 1975 1985 1995 2005t) -4.7mm/10yr

0

100

200

300

400

500

1955 1965 1975 1985 1995 2005

* means that the slope is significant. Capital letters A, B and C refers to the slope differences among biomes (p < 0.05)

Decadal Change of the RegionDecadal Change of the Region

Decade Tmean (C) Tmin (C) DTR (C) VPD (kPa) PPT (mm)

1955-1965 3.6 -3.0 13.9 0.55 318

1966-1975 3.5 (0.95) -3.0 (0.89) 13.7 (0.10) 0.57 (0.25) 286 (0.24)

1976-1985 3.8 (0.38) -2.5 (0.05) ↑ 13.2 (0.00) ↓ 0.58 (0.45) 303 (0.21)

1986-1995 4.5 (0.00)↑ -1.5 (0.00) ↑ 12.8 (0.00) ↓ 0.59 (0.27) 319 (0.51)

1996-2005 4.8 (0.04)↑ -1.3 (0.11) 13.0 (0.17) 0.64 (0.00) ↑ 290 (0.24)

1955-2005 4.0 -2.3 13.3 0.58 303

Arrows represent significant increasing or decreasing trends of a decade comparing to its proceeding one (p < 0.05).

Spatial VariabilitySpatial Variability

Solid circle means the trend is not significant (p > 0.05); open circle of different sizes means the differences in the rate of changes.

b) Rate of change in Tmin (˚C) /10yr

00.16 – 0.530.56 – 0.690.74 – 1.00

d) Rate of change in VPD (kPa) /10yr

00.008 – 0.0240.025 – 0.0360.055 – 0.061

a) Rate of change in Tmean (˚C) /10yr

00.01 – 0.340.35 – 0.520.54 – 0.71

c) Rate of change in DTR (˚C) /10yr

0-0.16 – -0.34-0.37 – -0.59-0.76 – -0.90

ConclusionsConclusions

IM has changed to a warmer and drier environment over the period of 1955-2005, with grassland and desert biomes experiencing stronger changes as compared to the forest biome.

The changes in the climate varied significantly by location and over time.

Temporal and Spatial Variability Temporal and Spatial Variability of Climate Extremes in Inner of Climate Extremes in Inner Mongolia from 1955 to 2005Mongolia from 1955 to 2005

Study Two

Introduction to Study TwoIntroduction to Study Two

Climate extremes are often the most sensitive measures of climate change (IPCC 2001).

Climate extremes can produce much stronger influences on ecological, societal and economic processes than means do (Katz et

al., 1992; Beniston and Stephenson, 2004).

However, our knowledge of the temporal and spatial variations in climate extremes is still not as conclusive as mean climate conditions.

ObjectivesObjectives

To evaluate the variations in the climate extremes in time and space in IM.

1. To detect the differences in the long-term trends of climate extremes among the three biomes (i.e., forest, grassland and desert);

2. To examine the inter-decadal variations and shifts in space; 3. To explore the dependency of the spatiotemporal changes on

geographical features such as latitude, longitude, and elevation.

MethodsMethodsExtreme Climate IndicesExtreme Climate Indices

Frich et al. (2002)

Abbr. Definition Unit Season of a Year

Extreme Temp

Indices (ETI)

ETRExtreme temperature range (intra-annual) : difference between the highest and lowest temperatures of a year

C summer & winter anomaly

FDFrost days: No. of days (d) with absolute minimum temperature <0 C d winter extreme low

GSLGrowing season length: period between when Tmean >5

C for >5 days and Tmean < 5 C for >5 daysd spring & fall anomaly

WNWarm night: No. of days with Tmin > 90th percentile of

daily minimum temperatured nighttime extreme low

HWDIHeat wave duration index: maximum period > 5 consecutive days with Tmax above 5 C compared to

1955-2005 daily Tmax normal daysd daytime extreme high

Extreme Precp

Indices (EPI)

CDDConsecutive dry days: maximum number of consecutive dry days (Rday < 1 mm) d dry season

RR1 No. of precipitation days (precipitation ≥ 1 mm/day) d wet season

wet season

wet season

wet season

SDIISimple daily intensity index: annual total of daily precipitation ≥ 1 mm / RR1

mm/d

R5d Maximum 5 day precipitation (total) mm

R75Wet days: no. of days when daily precipitation exceeding the 1955–2005 75th percentile

d

Statistical AnalysisStatistical Analysis

Least square linear regression to examine the long-term changes.

T-test with repeated procedure to examine the differences in the indices among decadal means.

Repeated regression analysis to examine the relationships between the magnitudes/trends of the indices and geographical features.

Spatial interpolations in selected indices using the method of regularized spline with tension (RST).

Temporal Changes at Regional and Temporal Changes at Regional and Biome Scales - ETIBiome Scales - ETI

Year

ETR FD GSL WN HWDI

R

F

G

D

- 0. 3 ° C/ 10yr

50

60

70

80

1955 1965 1975 1985 1995 2005

- 3. 7 d/ 10yr *

140

160

180

200

220

1955 1965 1975 1985 1995 2005- 0. 1 ° C/ 10yr

50

60

70

80

1955 1965 1975 1985 1995 2005

- 2. 1 d/ 10yr *

160

180

200

220

240

1955 1965 1975 1985 1995 2005- 0. 2 ° C/ 10yr

50

60

70

80

1955 1965 1975 1985 1995 2005

- 4. 0 d/ 10yr *

160

180

200

220

240

1955 1965 1975 1985 1995 2005- 0. 6 ° C/ 10yr *

50

60

70

80

1955 1965 1975 1985 1995 2005

- 4. 6 d/ 10yr *

140

160

180

200

220

1955 1965 1975 1985 1995 2005

3. 0 d/ 10yr *

150

170

190

210

1955 1965 1975 1985 1995 20052. 1 d/ 10yr *

150

170

190

210

1955 1965 1975 1985 1995 20053. 4 d/ 10yr *

140

160

180

200

1955 1965 1975 1985 1995 20053. 0 d/ 10yr *

160

180

200

220

1955 1965 1975 1985 1995 2005

0. 56 d/ 10yr *

0

5

10

15

1955 1965 1975 1985 1995 20050. 56 d/ 10yr *

0

5

10

15

1955 1965 1975 1985 1995 20050. 62 d/ 10yr *

0

5

10

15

1955 1965 1975 1985 1995 20050. 48 d/ 10yr *

0

5

10

15

1955 1965 1975 1985 1995 2005

6. 8 d/ 10yr *

0

20

40

60

80

1955 1965 1975 1985 1995 20054. 6 d/ 10yr *

0

20

40

60

80

1955 1965 1975 1985 1995 20057. 1 d/ 10yr *

10

30

50

70

90

1955 1965 1975 1985 1995 20057. 8 d/ 10yr *

0

20

40

60

80

1955 1965 1975 1985 1995 2005

B

B

A

B

A

A

A

A

A

B

A

A

A

A

A

Reg

ion

Fo

rest

Gra

ssla

nd

Des

ert

*means that the slope is significant. Capital letters A, B and C indicate the slope differences among biomes (p < 0.05)

Temporal Changes at Regional and Temporal Changes at Regional and Biome Scales - EPIBiome Scales - EPI

Year

R

F

G

D

CDD RR1 SDII R5d R75

1.2 d/10yr

0

50

100

150

1955 1965 1975 1985 1995 2005

-0.006 mm/d/10yr

3

5

7

9

1955 1965 1975 1985 1995 2005

-0.7 d/10yr

25

35

45

55

1955 1965 1975 1985 1995 2005

-0.9 d/10yr

20

40

60

80

100

1955 1965 1975 1985 1995 2005

-1.3 d/10yr*

40

50

60

70

80

1955 1965 1975 1985 1995 20051.2 d/ yr

0

50

100

150

1955 1965 1975 1985 1995 2005

-0.03 mm/d/10yr

6

8

10

12

1955 1965 1975 1985 1995 2005

-0.5 d/10yr

40

50

60

70

1955 1965 1975 1985 1995 2005

-1.7 d/10yr

50

70

90

110

130

1955 1965 1975 1985 1995 2005

-1.5 d/10yr*

50

60

70

80

90

1955 1965 1975 1985 1995 20051.6 d/10yr

0

50

100

150

1955 1965 1975 1985 1995 2005

-0.006 mm/d/10yr

4

6

8

10

1955 1965 1975 1985 1995 2005

-0.9 d/10yr*

30

40

50

60

1955 1965 1975 1985 1995 2005

-0.6 d/10yr

40

60

80

100

120

1955 1965 1975 1985 1995 2005

-1.3 d/10yr*

45

55

65

75

85

1955 1965 1975 1985 1995 20051.7 d/10yr

50

100

150

200

1955 1965 1975 1985 1995 2005

0.01 mm/d/10yr

3

5

7

9

1955 1965 1975 1985 1995 2005

-0.8 d/10yr

10

20

30

40

1955 1965 1975 1985 1995 2005

-1.5 d/10yr

0

20

40

60

80

1955 1965 1975 1985 1995 2005

-1.4 d/10yr*

30

40

50

60

70

1955 1965 1975 1985 1995 2005

Reg

ion

Fo

rest

Gra

ssla

nd

Des

ert

*means that the slope is significant (p < 0.05).

Spatial Variation of Trends - ETISpatial Variation of Trends - ETI

Circle size indicates the magnitude of the rate; black diamond indicates a significant change at p<0.05.

0↑, 51↓

51↑, 0↓

49↑, 2↓

49↑, 2↓

36↑, 15↓

Spatial Variation of Trends - EPISpatial Variation of Trends - EPI

Circle size indicates the magnitude of the rate; black diamond indicates a significant change at p<0.05.

15↑, 36↓

20↑, 31↓

31↑, 20↓

14↑, 37↓

24↑, 27↓

Geographical Influences on Geographical Influences on Climate ExtremesClimate Extremes

Indices LON LAT ELE

ETR -0.31*** 1.69 *** 0.001*

FD 1.17 *** 7.21 *** 0.040 ***

GSL -1.75 *** -6.18*** -0.041 ***

WN 0.34 -0.57 0.001

HWDI -0.05 ** 0.23 *** 0.001 **

CDD -6.34 *** 0.44 -0.071***

R5d 3.85 *** -3.02 *** -0.000

RR1 2.69 *** 0.42 ** 0.023 ***

SDII 0.22 *** -0.31 *** -0.0001 ***

R75 2.58 *** 1.59 *** 0.030 ***

Indices LON LAT ELE

ETR -0.01 * 0.01 * - 0.000

FD 0.003 0.007 0.000

GSL 0.02 *** -0.03 ** 0.000

WN -0.002 -0.008 0.000

HWDI 0.001 -0.001 - 0.000

CDD -0.004 0.013 - 0.000

RR1 -0.01 * 0.007 - 0.000

SDII 0.001 -0.001 0.000

R5d -0.008 0.037 * 0.000

R75 -0.02 *** 0.02 * - 0.000

Magnitude vs. longitude, latitude & elevation Trend vs. longitude, latitude & elevation

* p< 0.05, ** p<0.001, *** p<0.001.

Longitude gradient (from east to west): the warm and dry extremes increased; the cold and wet extremes decreased.Latitude gradient (from south to north): warm extremes decreased; cold extreme increased. PPT days increased and PPT density decreased.Elevation: similar to latitude

Spatial Interpolation - Decadal Spatial Interpolation - Decadal MeansMeans

ETI EPI


The hot extremes have increased and the cold extremes have decreased in IM in the past 50 years. The most significant changes occurred in the grassland and desert biomes.

The dry or wet extremes had no significantly changes in the region, with high temporal and spatial variability and inconsistent differences among the biomes.

With increasing longitude, the climate was getting warmer and drier; with increasing latitude or elevation, the climate was getting colder. The precipitation days increased but precipitation density decreased.

The trends in the extreme indices were mostly independent of the geographical gradients.

Potential Effects of Climate Change on the Ecosystems in IM

The warming and drying climate may affect ecosystems in various aspects in IM, such as reducing vegetation production and crop yield (Hou et al., 2008), reducing biodiversity (John et al., 2008) and aggravating desertification (Gao et al., 2003).

Ecosystem processes (land cover change) and climate feedbacks:

For example, a positive feedback between the warming-drying climate and decrease in ecosystem carbon storage.

Evapotranspiration and Soil Evapotranspiration and Soil Moisture Dynamics in Three Moisture Dynamics in Three

Paired Ecosystems in Semi-arid Paired Ecosystems in Semi-arid Inner MongoliaInner Mongolia

Study Three

Introduction to Study ThreeIntroduction to Study Three

In semi-arid and arid regions, evapotranspiration (ET) is the dominant component of water balance (Kurc and Small, 2004; Huxman et al., 2005).

Precipitation pulses control the dynamics of ET and the physiological responses of plants (Noy-Meir, 1973; Schwinning and Sala, 2004).

Cultivation and grazing are the two representative anthropogenic disturbances in IM.

Trees are naturally distributed only in the scattered areas with shallow groundwater in the semi-arid IM, but poplars were planted as fast-growing woods to combat desertification in IM.

The disturbances (or land cover change) are expected to alter ET, vertical distribution of soil water, and ET-soil water interactions due to the changes in species composition, vegetation cover and soil properties (Grayson and Western, 1998; Zhang and Schilling, 2006).


To evaluate the effects of three types of anthropogenic disturbances on:

1. the magnitude and temporal dynamics of ET; 2. the interaction between ET and soil water content; 3. the relative contribution of soil water storage (S) from

different soil layers to ET.

I hypothesize that cultivation, grazing and tree plantation have significant influences on the water cycles due to the changes in vegetation and soil properties.

Ecosystem-based Observations in Ecosystem-based Observations in Three Paired SitesThree Paired Sites

Xilinhot Fenced Grassland (Xf)

Xilinhot Grazed Grassland (Xd)

X

Fenced in 1999

Kubuqi Poplar Plantation (Kp)

Kubuqi Shrubland (Ks)

K

Planted in 2003

Duolun Cropland (Dc)

Duolun Grassland (Ds)

D

Reclaimed from 1970s

Disturbed vs. natural

MethodsMethods

Latent heat flux (LE), net radiation (Rn): EC system Soil heat flux (G): HFT-3 heat plates Air temperature (Ta) and relative humidity (Rh): HMP45AC probes Precipitation (PPT): TE525 tipping bucket rain gauge Wind speed (u): propeller anemometer (CSI) Volumetric water content (VWC): EasyAC50 probes (at 0-10, 10-20, 20-30, 30-50 cm) Leaf Area Index (LAI): portable area meter

)1(

)(273

)(408.0

2

2

0 uC

eeuT

CGRn

ETd

asa

n

FAO Penman-Monteith equation:

Water balance:

PPT = ET + S + R or

PPT – ET = S + R

R – water residual

Site Soil typeBulk density

(g cm-3)*Dominant species Ta (˚C) VPD (kPa) LAImax

Ds Chestnut 1.38 Stipa Krylovii, Artemisia frigida 13.8 0.6 0.92

Dc Chestnut 1.24 Triticum aestivum 13.3 0.7 2.42

Ks Sandy soil - Artermisia sp. 19.0 1.4 0.30

Kp Sand - Populus sp. 19.2 1.3 1.96

Xf Chestnut 1.22 Stipa grandis, Leymus chinensis 14.2 1.0 0.60

Xd Chestnut 1.33 Stipa grandis, Artemisia frigida 14.6 0.9 0.47

Site CharacteristicsSite Characteristics

* upper 20 cm of soil

Seasonal Changes of PPT, ET and Seasonal Changes of PPT, ET and Water YieldWater Yield

R2

Xf: 0.29 0.62Xd: 0.05 0.37

0

30

60

90

5/1-

5/10

5/11

-5/2

0

5/21

-5/3

16/

1-6/

106/

11-6

/20

6/21

-6/3

07/

1-7/

10

7/11

-7/2

07/

21-7

/31

8/1-

8/10

8/11

-8/2

0

8/21

-8/3

19/

1-9/

109/

11-9

/20

9/21

-9/3

010

/1-1

0/10

10/1

1-10

/20

10/2

1-10

/31

Ds Dc

0

10

20

30

40

50

5/1-

5/10

5/11

-5/2

0

5/21

-5/3

16/

1-6/

10

6/11

-6/2

06/

21-6

/30

7/1-

7/10

7/11

-7/2

07/

21-7

/31

8/1-

8/10

8/11

-8/2

0

8/21

-8/3

1

9/1-

9/10

9/11

-9/2

0

9/21

-9/3

010

/1-1

0/10

10/1

1-10

/20

10/2

1-10

/31

Ds Dc

- 30

0

30

60

5/1-

5/10

5/11

-5/2

0

5/21

-5/3

16/

1-6/

106/

11-6

/20

6/21

-6/3

07/

1-7/

107/

11-7

/20

7/21

-7/3

1

8/1-

8/10

8/11

-8/2

08/

21-8

/31

9/1-

9/10

9/11

-9/2

09/

21-9

/30

10/1

-10/

10

10/1

1-10

/20

10/2

1-10

/31

Ds Dc

0

30

60

90

5/1-

5/10

5/11

-5/2

0

5/21

-5/3

16/

1-6/

106/

11-6

/20

6/21

-6/3

07/

1-7/

107/

11-7

/20

7/21

-7/3

1

8/1-

8/10

8/11

-8/2

08/

21-8

/31

9/1-

9/10

9/11

-9/2

09/

21-9

/30

10/1

-10/

10

10/1

1-10

/20

10/2

1-10

/31

Ks Kp

0

10

20

30

40

50

5/1-

5/10

5/11

-5/2

05/

21-5

/31

6/1-

6/10

6/11

-6/2

06/

21-6

/30

7/1-

7/10

7/11

-7/2

07/

21-7

/31

8/1-

8/10

8/11

-8/2

08/

21-8

/31

9/1-

9/10

9/11

-9/2

0

9/21

-9/3

010

/1-1

0/10

10/1

1-10

/20

10/2

1-10

/31

Ks Kp

- 30

0

30

60

5/1-

5/10

5/11

-5/2

0

5/21

-5/3

16/

1-6/

106/

11-6

/20

6/21

-6/3

07/

1-7/

107/

11-7

/20

7/21

-7/3

1

8/1-

8/10

8/11

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PPT

(m

m)

PPT

-ET

(m

m)

ET

(m

m)

R2

Ds: 0.55 0.56Dc: 0.67 0.65

R2

Ks: 0.37 0.46Kp: 0.18 0.05

050

100150200250300350400450

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Cum

ulat

ive

PPT

, ET

(m

m)

Date

Cumulative PPT, ET and Cumulative PPT, ET and S S

Site PPT ET PPT-ET ET/PPT

Ds 403.2 352.7 50.5 0.87

Dc 393.7 315.2 78.5 0.80

Ks 220.5 221.7 -1.2 1.01

Kp 147.8 236.4 -87.6 1.60

Xf 150.2 216.8 -66.6 1.44

Xd 185.0 217.2 -32.2 1.18

050

100150200250300350400450

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Xf_0-10cmXf_RXd_0-10cmXd_R

Cum

ulat

ive S

, R (

mm

)

Date

Effects of VWC on ET and ET/PETEffects of VWC on ET and ET/PET

Soil layer (cm)

All NR All NR All NR All NR All NR All NR

Ds ET Dc ET Ks ET Kp ET Xf ET Xd ET

0-10 0.42 0.62 0.45 0.56 0.57 0.74 0.04 0.08 0.29 0.45 0.43 0.44

10-20 0.46 0.70 0.50 0.62 0.45 0.62 0 0 n/a

20-30 0.45 0.66 0.53 0.70 0.27 0.38 0.02 0.04

30-50 0.13 0.48 0.23 0.37 0.13 0.18 0.01 0.03

0-50 0.44 0.72 0.49 0.68 0.35 0.47 0.01 0.03

Ds ET/PET Dc ET/PET Ks ET/PET Kp ET/PET Xf ET/PET Xd ET/PET

0-10 0.12 0.49 0.25 0.41 0.36 0.61 0.02 0.04 0.19 0.34 0.37 0.44

10-20 0.13 0.60 0.27 0.43 0.38 0.61 0.02 0.04 n/a

20-30 0.12 0.53 0.23 0.47 0.32 0.50 0.01 0.03

30-50 0.02 0.31 0.08 0.27 0.23 0.33 0.02 0.03

0-50 0.12 0.57 0.23 0.49 0.35 0.55 0.02 0.04

All: all observations included, NR: observations during rainy periods excluded

Relative Contribution of Relative Contribution of ∆S∆S in Soil in Soil Vertical Profile to ETVertical Profile to ET

∆S from 0-10, 10-20, 20-30, 30-50 cm soil contributed varied percents of water to total ET at different site:

– Ds: 40%, 24%, 6%, and 0% (66%)– Dc: 15%, 10%, 5% and 11% (42%)

– Ks: 16%, 15%, 6% and 0% (37%)– Kp: 3%, 0%, 0% and 0% (3%)

– Xf : 38% (>38%)– Xd: 27% (>27%)

Correlation Between Root Biomass Correlation Between Root Biomass

and Relative Contribution of and Relative Contribution of ∆S∆S to to ETET

Root biomass (g m-2)

Perc

ent o

f S/

ET (%

)

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800 1000 1200 1400 1600

Ds

Dc

Xd

Xf

R2=0.72, p<0.01

Pattern of water flow through root system during day and night (Caldwell, 1988).

Hydraulic Lift HypothesisHydraulic Lift Hypothesis


Cultivation and grazing tended to decrease ecosystem ET of the growing season due to the decreased ∆S in the upper soil layers where the roots were mainly distributed.

Poplar plantation increased ET most probably because the poplars accessed the groundwater by the deep roots.

Changes in growing length and LAI also accounted for the ET difference between sites.

Downscaling AMSR Soil Moisture Downscaling AMSR Soil Moisture Using MODIS Indices in Semi-arid Using MODIS Indices in Semi-arid

Inner MongoliaInner Mongolia

Study Four

Introduction to Study FourIntroduction to Study Four

Spatial variability of available soil moisture (Ms) is the key factor influencing vegetation distribution, ecosystem structure, function and diversity (Grayson et al., 1997; Yeakley et al., 1998; Baudena et al., 2007).

The precision of current spatial models to simulate carbon, energy and water fluxes are mostly poor due to the lack of spatial Ms data.

The errors in Ms estimations contributed substantial uncertainties to model output (Xiao et al., 1997; Zhang et al., 2009).

Conventional MethodsConventional Methods

Point measurements: predominantly developed for applications in agriculture to understand field-scale soil water dynamics, such as time-domain reflectometry (TDR) techniques.

Remote sensing technology: developed for understanding the hydrology of land–surface–atmosphere interactions, especially at river basin, continental, and global scales (Kerr et al., 2001).

Gaps in Ms DatabaseGaps in Ms Database

The current techniques of Ms measurement have limitations in providing sufficient spatial resolution or coverage of intermediate scales (Qiu et al., 2000; William et al., 2003; English et al., 2005).

It is pertinent to bridge between the Ms measurements and data requirements in ecosystem and regional studies.

Advanced Microwave Scanning Radiometer - EOS (AMSR-E) (C band, 6.9 GHz): Global coverage Spatial resolution of 25 km


(1) To evaluate the relationship between AMSR-E derived Ms and MODIS-derived indices in three land use/cover (LULC) types in semi-arid IM;

(2) To investigate the capability of MODIS products (500 m or 1000 m) as proxies of AMSR Ms so that finer-resolution Ms can be estimated.

6251250

MethodsMethods

Excluded grids of cropland cover > 60% & NDVI > 0.5

(Jackson, 2002).

LULCLULCNDVINDVIEVIEVINDSVINDSVILSTLST 1 km1 km

25 km25 km

Variable Product name Data source Spatial resolution

Temporal resolution

Soil moisture Level 3 AMSR-E National Snow and Ice Data Center

0.25˚ (~25 km ) daily

NDVI/EVI/NDSVI MOD09A1 EOS data gateway 500 m 8-day composite

LULC MOD12Q1 EOS data gateway 1000 m 8-day composite

LST MOD11A2 EOS data gateway 1000 m 8-day composite

Precipitation TRMM Goddard DAAC 0.25˚ (~25 km ) daily

Biome boundary World Wildlife Fund (WWF) Terrestrial Ecoregions

2004

Class definition: X > 50% of the grid area

Statistical AnalysisStatistical Analysis

ANOVA with repeated procedure to test Ms differences among LULC types.

Non-intercept linear regression analysis between Ms and the grid-mean EVI and NDSVI (VI).

Paired t-test for testing the differences of Ms-VI regression slopes.

Multivariate stepwise regression: Ms = f (EVI, EVIsd, NDSVI, NDSVIsd, LST, LSTsd)

07/22/04

06/18/04

05/11/04

04/28/04

……• Three Ms images were randomly selected for each month (one for every ten days) from

April to October in 2004 (21 in total);

• NDVI, EVI, NDSVI and LST products were selected according to the dates of Ms.

Data SelectionData Selection

Ms in Three LULC Ms in Three LULC TypesTypes

G – grassland S – shrubland C – cropland

Season Spring Summer Fall

Land cover G S C G S C G S C

N 3013 420 296 4936 672 359 3152 365 292

Ms 0.11b 0.09c 0.12a 0.11b 0.09c 0.14a 0.10b 0.07c 0.12a

Mssd 0.02 0.02 0.02 0.03 0.02 0.03 0.03 0.02 0.02

Msmax 0.2 0.14 0.23 0.28 0.23 0.24 0.28 0.13 0.21

Msmin 0.06 0.06 0.08 0.04 0.04 0.06 0.03 0.04 0.06

Ms – VI RelationshipMs – VI Relationship

a)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

4/1 5/1 5/31 6/30 7/30 8/29 9/28 10/28

K'(E

VI)

Kg'(EVI) Ks'(EVI) Kc'(EVI)

b)

0

0.1

0.2

0.3

0.4

0.5

4/1 5/1 5/31 6/30 7/30 8/29 9/28 10/28

Date

K'(N

DS

VI)

Kg'(NDSVI) Ks'(NDSVI) Kc'(NDSVI)

K’: non-intercept slope of the linear regression between Ms and (a) EVI and (b) NDSVI

Effects of Temperature on Effects of Temperature on K’K’

a)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50L S T

K'(E

VI)

b)

0

0.1

0.2

0.3

0.4

0.5

0 10 20 30 40 50L S T

K'(N

DS

VI)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

-10 0 10 20 30 40

K s '(E V I)K g'(E V I)K c '(E V I)

线性 (K s '(E V I))

线性 (K g'(E V I))

线性 (K c '(E V I))

Linear

Linear Linear

Kg’(EVI): 36% (p=0.005)Ks’(EVI): 9% (p=0.30) Kc’(EVI): 20% (p=0.04)

Empirical ModelsEmpirical Models

Season Spring Summer Fall

Land cover Parameter Partial R2 Parameter Partial R2 Parameter Partial R2

Grassland

NDSVI 0.34 NDSVI 0.26 NDSVI 0.31

LST 0.05 EVI 0.09 LST 0.08

EVIsd 0.03

Total R2 0.39 Total R2 0.38 Total R2 0.39

Shrubland

LST 0.11 NDSVI 0.14 NDSVI 0.40

EVI 0.05 LST 0.04

LSTsd 0.06


Cropland

NDSVI 0.42 NDSVI 0.25 NDSVI 0.24

LST 0.05 EVI 0.04 LST 0.03

LST 0.05


Conclusions Conclusions

The Ms-EVI relationship varied over the growing season and among the LULC types.

The Ms-NDSVI relationship was relatively constant; and NDSVI appeared to be the primary predictor of surface Ms for all three LULC types.

The empirical models for predicting Ms using MODIS indices were plausible, which provided an insight to estimate finer-resolution Ms at a large spatial scale.

Summary: Lessons Learned from the Summary: Lessons Learned from the StudiesStudies

Land Use EffectsClimate Effects

SiB3

com

pa

riso

n

time

Regional Database

energy

comparison

MODIS(VI, albedo, T)

regional RS modeling

stable isotopepartitioning

Land Cover

landscape

Landsat ETM+

spa

ti al

pa

r am

et e

riza

t ion

scenarios

Tower(ET, Rn, G)







Mobile EC Tower ecosystem 1 in FY1ecosystem 2 in FY2ecosystem 3 in FY3

landscape 3landscape 1 landscape 23 ecosystemslandscape 1-3

Energy Mobile Towerlandscape 1 in FY 1landscape 2 in FY 2landscape 3 in FY 3

VegetationSoil

Climate

Public Web Acess

com

pa

r iso

n

supervisedclassification

QA/QC

Task 4

Task 3

Task 4

Task 1

Task 2

scenariosE, Tr, EF

water

* Study 1, 2

* Study 3

* Study 4

AcknowledgementsAcknowledgements This research was conducted as part of the Northern Eurasia Earth Science

Program Initiative (NEESPI) and supported by the National Aeronautics and Space Administration (NASA) and the US-China Carbon Consortium (USCCC).

Collaboration institution: Institute of Botany, Chinese Academy of Science (IBCAS).

Advisor: Dr. Jiquan Chen

Committee members: Dr. Daryl Moorhead, Dr. Scott Heckathorn, Dr. Kevin Czajkowski, Dr. Asko Noormets and Dr. Ge Sun.

Fellow lab mates: especially Dr. Burkhard Wilske, Ranjeet, Jessica, Jianye, Mike, Rachel and Gwen.

Dr. James Harrell, Dr. Christine Mayer, Dr. Ann Krause, Dr. Elliot Tramer, Dr. Daryl Dwyer, Lisa, Dan, Malak, Zach, Chongfeng and Haiqiang.

My family.

Thanks!Thanks!

Spatial Changes by Decade

ETI EPI

0

10

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40

1 2 3 4 5

<55

56-60

61-65

66-70

>70

0

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<160

161-190

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221-250

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>200

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<35

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56-65

>65

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40

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<5.0

5.1-6.0

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>8.0

0

10

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1 2 3 4 5

<20

21-40

41-60

61-80

>80

a) d)

b)

c)

e)

f)

Decade Decade

Perc

ent o

f Are

a (%

)

ETR

FD

GSL

R75

SDII

R5d

0.0000

10.0000

20.0000

30.0000

40.0000

50.0000

fore

st

shru

bland

sava

nna

grassla

nd

perm

anen

t wetla

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cropla

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urban

and b

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natur

al ve

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tion

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anen

t snow

barre

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area

(sq

. km

)

1992 2001 2004

R John, J Chen, N Lu, et al submitted “Land cover / land use change in Inner Mongolia: 1992-2004”

-20000.00

-10000.00

0.00

10000.00

20000.00

30000.00

area

(in

sq

.km

)

2001-1992

-20000.00

-10000.00

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10000.00

20000.00

area

(sq

. km

s)

2004-2001

Land Use and Land Cover Change in IM

±0 250 500 750125 Km

1992

2001

2004

Microwave Remote Sensing Microwave Remote Sensing MsMs

Passive microwave signal offers several advantages over other methods for remote sensing Ms (Draper et al., 2009).

The long wavelength can penetrate through cloud cover, haze and dust.

It has a direct relationship with Ms through the soil dielectric constant.

It has a reduced sensitivity to land surface roughness.

landscape ecology and ecosystem science (lees) lab department of environmental sciences

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