landscape ecology and ecosystem science (lees) lab department of environmental sciences the...
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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)
Tower 1.1 (less disturbed)
Tower 1.2 (intensively disturbed)
Tower 2.1 (less disturbed)
Tower 2.2 (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
ConclusionsConclusions
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).
ObjectivesObjectives
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
-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
30
60
90
5/1-
5/10
5/11
-5/2
0
5/21
-5/3
16/
1-6/
10
6/11
-6/2
0
6/21
-6/3
07/
1-7/
10
7/11
-7/2
0
7/21
-7/3
18/
1-8/
10
8/11
-8/2
0
8/21
-8/3
19/
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
Xf Xd
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
0
6/21
-6/3
07/
1-7/
10
7/11
-7/2
0
7/21
-7/3
18/
1-8/
10
8/11
-8/2
0
8/21
-8/3
19/
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
Xf Xd
- 30
0
30
60
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
Xf Xd
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
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
Ds_PPTDs_ETDc_PPTDc_ET
-60
-30
0
30
60
90
120
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28-120
-90
-60
-30
0
30
60
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
0
50
100
150
200
250
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
Ks_PPTKs_ETKp_PPTKp_ET
0
50
100
150
200
250
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
Xf _PPTXf _ETXd_PPTXd_ET
-80
-60
-40
-20
0
20
40
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
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
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
-60
-30
0
30
60
90
120
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
Ds_0-50cmDs_RDc_0-50cmDc_R
-120
-90
-60
-30
0
30
60
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
Ks_0-50cmKs_RKp_0-50cmKp_R
0
50
100
150
200
250
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 280
50
100
150
200
250
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
-80
-60
-40
-20
0
20
40
5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28
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
ConclusionsConclusions
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
ObjectivesObjectives
(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
Total R2 0.22 Total R2 0.14 Total R2 0.44
Cropland
NDSVI 0.42 NDSVI 0.25 NDSVI 0.24
LST 0.05 EVI 0.04 LST 0.03
LST 0.05
Total R2 0.47 Total R2 0.34 Total R2 0.27
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)
Tower 3.1 (less disturbed)
Tower 3.2 (intensively disturbed)
Tower 1.1 (less disturbed)
Tower 1.2 (intensively disturbed)
Tower 2.1 (less disturbed)
Tower 2.2 (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
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
20
30
40
1 2 3 4 5
<55
56-60
61-65
66-70
>70
0
10
20
30
40
50
1 2 3 4 5
<160
161-190
191-220
221-250
>250
0
10
20
30
40
1 2 3 4 5
<140
141-160
161-180
181-200
>200
0
10
20
30
40
1 2 3 4 5
<35
36-45
46-55
56-65
>65
0
10
20
30
40
1 2 3 4 5
<5.0
5.1-6.0
6.1-7.0
7.1-8.0
>8.0
0
10
20
30
40
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
nds
cropla
nds
urban
and b
uilt-u
p
natur
al ve
geta
tion
perm
anen
t snow
barre
n
water
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
0.00
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.