summary & remarks session 1: extreme events: detection · (2000 to near present at 5 km*5 km...
TRANSCRIPT
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Summary & Remarks
Session 1: Extreme Events: Detection
Professor Z. (Bob) SuITC, University of TwenteThe [email protected]
www.itc.nl/wrs
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Hypothesis: A marked increase in the frequency
of extreme hydrologic events during the past
decades.
Present Evidence on changes in: precipitation, soil
moisture, evapotranspiration, streamflow, water
table, lake levels, agriculture, vegetation, and social-
economic factors
Use in situ measurements, surveys, and satellite
remote sensing & document errors in
observations
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2 Keynotes: (M. Rodell, J. Nielsen-Gammon)
2 Orals
Posters (S1 &S2)
What are extreme events?
What are the relevant spatiotemporal scales?
What are the causes?
• GRACE & DA: Storages - Droughts/floods can be detected
on monthly interval
• Bias correction: high res. SPI blend - detection of drought
development
• Integration diff. system for flood monitoring & prediction
• Development of climate change adaptation measures
• Estimation of reservoir storages & monitoring soil moisture
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What are extreme events?
(Su, et al., 2010, Treatise on Water Science)
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ITC GEO Soil Moisture Soil Temperature Networks
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Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs)
Su, Z., et al. 2011, HESS
ESA Dragon programme
EU FP7 CEOP-AEGIS project
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ITC Earth Observation Research and Education SitesThe Role of the Tibetan Plateau in Global Climate(Collaboration with Chinese Academy of Sciences)
GEWEX Asian Monsoon Experiment (GAME) on the Tibet Plateau (GAME/Tibet,1996-2000)
CEOP (Coordinated Enhanced Observing Period) Asia-Australia Monsoon Project on the Tibetan Plateau (CAMP/Tibet, 2001-2005)
Tibetan Observation and Research Platform (TORP, 2005 -2010)
Third Pole Environment (2009 -2019)Coordinators: Y.Ma & T.Yao (ITP/CAS)
ESA Dragon programme
EU FP7 CEOP-AEGIS project
(Su, et al., 2011, HESS)
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Maqu: station description
8
5 cm ->
10 cm ->
20 cm ->
40 cm ->
& 80 cm
• 2/3 soil moisture & temperature probes
• 5, 10 & 20 cm deep (few profiles deep 80 cm)
• 1 datalogger
• data collected every 15 min
• memory capacity of 1 year
• completely buried
• site revisit to download data:
- beginning and end of monsoon season in Maqu
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Soil moisture at 5 cm depth of all the stations
9
Organic soils
Sandy loam soil
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Quantification of uncertainties in global products (Su, et al., 2011)
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How good is soil moisture analysis/assimilation? (Su & de Rosnay, et al. 2013)
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How good is soil moisture assimilation? An example in the Maqu area on Tibetan Plateau
Soil moisture from the ECMWF-EKF-ASCAT 2 run (using the EKF soil
moisture analysis with ASCAT data assimilation)
(Su & de Rosnay, et al. 2013)
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Noah LSM
14
Characteristic for the Noah LSM is that it provides a
complete description of the physical processes using a
limited number of parameters. Soil water flow;
Soil heat flow;
Heat exchange with the atmosphere;
(Zheng et al., 2013, JHM; 2014a,b,c in prep.)
Snow pack.
(Malik et al., 2012, JHM; JGR, 2013; RSE, 2011)
Frozen soil; ???
N: National Centers for Environmental Prediction (NCEP)
O: Oregon State University (Dept of Atmospheric Sciences)
A: Air Force (both AFWA and AFRL - formerly AFGL, PL)
H: Hydrologic Research Lab - NWS (now Office of Hydrologic Dev -- OHD)
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An Improved Two-layer Algorithm for Estimating Effective Soil Temperature
using L-band Radiometry (Lv et al., 2013, RSE)
effB TT
dxxdxaxxTTx
eff
0 0exp
2
1
24
xxx
0010
BB
eff eTeTT
110 xB
A two-layer system:
2
4exp1
exp1
1
1
0
x
x
eCB
(Wilheit 1978)
(Ulaby et al. 1978; 1979)
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The weight function C is a parameter affected by wavelength (a), soil moisture
(b), sampling depth (c), and soil temperature (d)
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SMAP – Simultaneous Modeling Of Active And Passive Microwave Signatures
To use a single discrete scattering model to simulate both emission and
backscattering, with a unique set of input parameters
To combine the use of active and passive microwave satellite signatures
to constrain the model
To contribute to an optimal use of SMAP-like data
To improve the soil moisture retrieval
University of Rome “Tor Vergata”
L. Dente, P. Ferrazzoli, Z. Su, R. van de Velde, L. Guerriero, 2013, Combined use of active and passive microwave satellite data to constrain a discrete scattering model, RSE, In press
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RESULTS: MODEL CALIBRATION (2009) – ACTIVE CASE
R2 = 0.9
rmse = 0.5 dB
bias = 0.2 dB
R2 = 0.9
rmse = 0.5 dB
bias = -0.04 dB
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RESULTS: MODEL CALIBRATION (2009) – PASSIVE CASE (1)
R2 = 0.8
rmse = 6.3 K
bias = 2.7 K
R2 = 0.5
rmse = 5.9 K
bias = 4.3 K
Surface temperature derived from V pol Ka-band AMSR-E Tb
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IF ONLY THE ACTIVE MICROWAVE DATA WERE USED …
TVG smooth surface and no litter
TVG smooth surface and no litter
… a good match with ASCAT observations was possible
with unrealistic assumptions:
- absence of litter
- smooth surface
However, the same assumptions led to a large underestimation of Tb!
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ESA STSE programme:
Water Cycle Multimission Observation Strategy (WACMOS)
Clouds
(Precipitation, 100%)
Water Storage
in Ice and Snow
Groundwater Storage & Flow
Discrharge to ocean
(35%)
Soil Moisture
Radiation
Water vapour
(transport to land 35%,
condensation 65%)
Evaporation
(land 65%, ocean 100%)
Water Resources
Management
21
Su et al., 2014, JAG
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Can we observe the spatiotemporal distributions of actual land
surface energy balance terms and ET?
Satellite dataLST, NDVI, Albedo…………
Meteorological forcing dataTair, Ws, Rh, Pres…..
Radiation forcing dataSWD, LWD…..
SEBS H, LE, G, Rn
Evaluation
In-situ data
Data quality control
Parameterization
improvement
(Processing chain developed in ESA WACMOS.org project, Su et al., 2014, JAG )
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SEBS input and output variables vs measurement at Yucheng station
winter wheat and summer maize
12 2 4 6 8 1012 2 4 6 8 10120
100
200
300(a)
SW
D (
W/m
2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 10120
20
40
60(b)
SW
U (
W/m
2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 1012200
300
400
500(c)
LW
D (
W/m
2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 1012200
300
400
500(d)
LW
U (
W/m
2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 1012-50
0
50
100
150(e)
Rn
(W
/m2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 1012
0
50
100
150(f)
H(W
/m2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 1012
0
50
100
150(g)
LE
(W/m
2)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 1012260
280
300
320(h)
LS
T(K
)
Month of 2003-2004
12 2 4 6 8 1012 2 4 6 8 10120
0.2
0.4
0.6
0.8(i)
Alb
edo
Month of 2003-2004
SEBS
Obs
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Seasonal average maps of sensible heat flux (H)(a) Mar-May, (b) Jun-Aug,(c) Sep-Nov, (d) Dec-Feb
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Spatiotemporal (climatic?) trends
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Spatiotemporal (climatic?) trends
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What is the characteristics of PBL on Tibetan Plateau
(Chen et al., 2013, PLOSone)
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Never ending human activities
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Land Surface & Water Use
River Outflow
Groundwater
Changes in Water Budget
Rain Snowevaporation
Surface Water
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Yangtze River Basin
30
•Upper Yangtze reach, from Tuotuohe, to Yichang.
•Middle reach from Yichang to Hukou.
•Lower reach extends from Hukou to the river mouth near Shanghai.
•Cuntan, Yichang, Hankou, and Datong are four gauging stations located along
the mainstream of the Yangtze.
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Upper reach TWS anomaly
(v: RL04 ssv201008)
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Cumulative TWS anomaly at Upper Reach (Yichang station)
GRACE data
(V: RL05.DSTvSCS1401)
(v: RL04 ssv201008)
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Impacts and projections in the Yangtze River Basin
Q1: What are observed
impacts to water
resources in Yangtze due
to climate and human
changes ?
Q2: Will the changes in
the Yangtze River Basin
influence the East Asian
monsoon patterns?
Q3: What will be the
spatial/temporal
distribution of water
(sediment) resources in
21st century ?
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ITC SEBS DERIVED GLOBAL ENERGY & ET FLUXES
(2000 to near present at 5 km*5 km spatial resolution), data access: linkendin SEBS group
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Describe
• Trends (change)
• Variability (natural cycle)
• Outliers
Understand
• Attribution (variability vs. error)
• Consistency Process (e.g. Volcanic eruption, fire/aerosol)
• Feedback links (e.g. ENSO teleconnection)
A Roadmap From Process Understanding To AdaptationClimate Change Adaptation In Water Resources
Detect
• Hot Spot
• Quality issue
• Outside Envelope
Predict
• Impacts
Adapt
• Consequences