Chinese Cryosphere Information System

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Chinese Cryosphere Information System
Li Xin
Cold and Arid Regions Environment and Engineering
Research Institute, CAS
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1. Introduction Cryosphere and Climate Change
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2. Structure of CCIS
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Hardware and Software Environment in CCIS
ARC/INFO
PC
ArcView
UNIX Workstation
NT Workstation
Applications in different environment
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3. Main Case Study Areas of CCIS
Qinghai-Tibet Highway
Qinghai-Tibet Plateau
Urumqi River Basin, Tienshan Mountains
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3.1 CCIS Qinghai-Tibet Plateau
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DEM
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Map of Frozen Ground
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Vegetation Map of the QTP
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3.2 CCIS Regions along the Qinghai-Tibet highway
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Database of frozen Soil Engineering Properties
along the Qinghai-Tibet Highway
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3.3 CCIS Urumqi River Basin in the Tienshan
Mountains
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DEMs of Glacier
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4. Spatial Interpolation of Climatic Variables

• Missing data estimation
• Data gridding

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Classification and Procedure of Spatial
Interpolation

• Geometric method
• Statistical method
• Geostatistical method
• Functional method
• Stochastic simulation
• Physical model simulation
• Combined method

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Meteorological Stations in the Qinghai-Tibet
Plateau
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Inverse Distance Weighting
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Interpolation results of inverse distance square
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Trend Surface
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Interpolation results of trend surface
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Kriging method
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Exploratory Spatial Data Analysis

• The mathematical expectation of the difference
  between two points separated by distance h is
  zero

• The variance of the difference between two points
  separated by distance h is minimized as

• Hence, the semi-variance can be calculated from
  data samples by the following equation

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Basic variogram models
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Cokriging method

• cokriging introduces a new hypothesis, the
  variance of the difference between two variables
  is minimized

• The equation of cross-variogram is as follows

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Fit of simple and cross-variogram
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Interpolation results of cokriging
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Combined method

• Assuming that spatial variable consists of three
  components one structural component, one
  stochastic and spatial correlated component, and
  one stochastic noise or residual. Let x denotes a
  two-dimensional or three-dimensional vector,
  spatial variable Z(x) can be expressed as

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Lapse rates of different latitudinal and
altitudinal zones in the Qinghai-Tibet Plateau
(?C/100m)
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a sum of nugget, linear and Gaussian variogram
models
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Conclusion

• Spatial interpolation is a very important spatial
  analysis tool in GIS. As for the cryospheric
  regions with sparsely and irrationally
  distributed meteorological stations, spatial
  interpolation is a basic method for the study of
  spatial distribution of climatic variables and
  also a prerequisite for the establishment of
  cryospheric models based on GIS.
• There is no absolutely optimal spatial
  interpolation method there is only relatively
  optimal interpolation method in special
  situation. Hence, the best spatial interpolation
  method should be selected in accordance with the
  qualitative analysis of the data, exploratory
  spatial data analysis and repeated experiments.

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5. Response of Permafrost to Global Change on the
Qinghai-Tibet Plateau - A GIS Aided Model
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Altitude Model
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Data Flow in the Altitude Model
Geo thermal Regime
GCM Scenarios
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DEM of Qinghai-Tibet Plateau
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Diagram of HADCM2
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The Air Temperature Rise on the Qinghai-Tibet
Plateau in 2049
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Nelson Frost Number Model
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Assumptions

• The Gaussian function that describes high
  altitude permafrost distribution will not change
  according to the climate warming.
• If air temperature increases 1?C, the vertical
  zonation will rise a certain height agreeing on
  the lapse rate, the lower limit of the
  high-altitude permafrost will rise the same
  height. Therefore, a relation can be established
  between the air temperature rise (?T) and the
  increased height of permafrost lower limit (?H).
  The relation is

• Lakes, glaciers, deserts will not change

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Permafrost Change when Air Temperature Rise 0.51C
Permafrost Change when Air Temperature Rise 1.10C
Permafrost Change when Air Temperature Rise 2.91C
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Permafrost Change on the Qinghai-Tibet Plateau
8.03
18.45
58.18
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6. Other Models
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6.1 Solar Radiation Model over Rugged Terrain

• Duration of possible sunshine Odefined as the
  set of duration when the sloping grid can receive
  direct solar radiation during the entire day.
• Isotropic view factor Visodefined as the ratio
  of the area of the visible part to the area of
  semi-sphere on a sloping grid. It stands for the
  influence of the surrounding terrain to the
  isotopic diffuse radiation.
• Circum-solar view factor V1defined as the ratio
  of the exoatmospheric radiance obstructed by
  surrounding and self-shadowing to the
  exoatmospheric radiance obstructed only by
  self-shadowing. It stands for the influence of
  the surrounding terrain to the circum-solar
  diffuse radiation.
• Shape factor Fijdefined as the ratio of the
  energy reached another sloping grid to the energy
  emitted from the source sloping grid.

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Duration of Possible Sunshine
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Obstruction of solar radiation by the surrounding
terrain
z
y
x
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Ray trace algorithm
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Isotropic View Factor
z
y
x
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Shape Factor
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????????
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Spectral reflectance Inverse
--with a Windows 95 Style User Interface
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Obstruct in winter
Obstruct in summer
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Result - Net Solar Radiation
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6.2 Relationship between mass balance, solar
radiation and air temperature
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Relationship between mass balance, solar
radiation and air temperature of Glacier No. 1

• Bj84471.3T 37.3Ip
• correlation coefficient (R)0.9121 R20.8320

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lt-5?C Extreme Stable type -5?C to -3?C Stable
type -3?C to -1.5?C Sub-stable type -1.5?C to
-0.5?C Transit type -0.5?C to 0.5?C Unstable
type gt0.5?C Extreme unstable type
Present
2009
2049
2099
6.3 Change of Permafrost-Engineering Properties
along the Qinghai-Tibet Highway (Tutuhe )
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The change of permafrost stability alongthe
Qinghai-Tibet Highway
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6.4 A Distributed Calculation Method for Glacier
Volume Change
1964
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Calculation of glacier mass balance using GIS
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Thanks