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Applied Hydrology Climate Change and Hydrology

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Applied Hydrology Climate Change and Hydrology Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University – PowerPoint PPT presentation

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Title: Applied Hydrology Climate Change and Hydrology


1
Applied HydrologyClimate Change and Hydrology
  • Professor Ke-Sheng Cheng
  • Department of Bioenvironmental Systems
    Engineering
  • National Taiwan University

2
Global Circulation Models (GCMs)
  • Computer models that
  • are capable of producing a realistic
    representation of the climate, and
  • can respond to the most obvious quantifiable
    perturbations.
  • Derived based on weather forecasting models.

3
Weather forecasting models
  • The physical state of the atmosphere is updated
    continually drawing on observations from around
    the world using surface land stations, ships,
    buoys, and in the upper atmosphere using
    instruments on aircraft, balloons and satellites.
  • The model atmosphere is divided into 70 layers
    and each level is divided up into a network of
    points about 40 km apart.

4
  • Standard weather forecasts do not predict sudden
    switches between stable circulation patterns
    well. At best they get some warning by using
    statistical methods to check whether or not the
    atmosphere is in an unpredictable mood. This is
    done by running the models with slightly
    different starting conditions and seeing whether
    the forecasts stick together or diverge rapidly.

5
  • This ensemble approach provides a useful
    indication of what modelers are up against when
    they seek to analyses the response of the global
    climate to various perturbations and to predict
    the course it will following in the future.
  • The GCMs cannot represent the global climate in
    the same details as the numerical weather
    predictions because they must be run for decades
    and even centuries ahead in order to consider
    possible changes.

6
  • Typically, most GCMs now have a horizontal
    resolution of between 125 and 400 km, but retain
    much of the detailed vertical resolution, having
    around 20 levels in the atmosphere.
  • Challenges for potential GCMs improvement
  • Modeling clouds formation and distribution
  • Tropical storms (typhoons and hurricanes)
  • Land-surface processes
  • Winds, waves and currents
  • Other greenhouse gases

7
GCMs
8
  • From GCMs to hydrological process modeling
  • Study of hydrological processes requires spatial
    and temporal resolutions which are much smaller
    than GCMs can offer.
  • Downscaling techniques have been developed to
    downscale GCM outputs to desired scales.
  • Dynamic downscaling
  • Statistical downscaling

9
Weather Generator of daily rainfall simulation
  • Markov chain for rain day/no-rain day simulation
  • Exponential distribution for daily rainfall
    simulation.

10
  • (?????????)

11
Effect of climate change on storm characteristics
  • Storm types
  • Convective storms
  • Typhoons
  • MCS (Mei-yu)
  • Frontal systems
  • Assessed based on MRI high-resolution outpots
    (dynamic downscaling)

12
???????????????????????
  • TCCIP Team 3
  • ?????

13
??
  • ????????????????????,???????????????????????
  • ????????????,??????????,???????????
  • ????
  • ??????
  • ????????

????? (?MRI)
?????
14
????????????
  • ??
  • ????(?2mm/hr)
  • ????(?12 hours)
  • ????
  • ????
  • ????
  • ????
  • ????????

15
??????
????????
  • ??????
  • ????
  • ????
  • ??????
  • ????????
  • ??????????
  • ?????????????????????(??)
  • ?????????????????????????????(1/2)(??)
  • ????????????????????????????? (??)
  • ??????????????????????????(??)
  • ???????????????????????????(1/2)(??)

16
MRI-WRF-5km??? (??1979-2003)
??????? (??1979-2003)
MRI-WRF-5km?????????????????
MRI-WRF-5km??? (???2015-2039)
MRI-WRF-5km??? (???2075-2099)
17
????????(????)
  • ???????????,?????????
  • ?????1 ????????0.5mm???????
  • ??????

???? ?? ??
??? (??) 5?-6? ???? gt 3?? ??? gt 0.5 mm/hr
??? (??) 7?-10? ???? gt 8?? ??? gt 2.5 mm/hr
??? (??) 7?-10? 3 ?? gt ???? 8?? ??? gt 2.5 mm/hr
??? (??) 11???4? ???? gt 4?? ??? gt 0.5 mm/hr
18
????
  • ????
  • ??1979-2003???
  • ??84?
  • MRI-WRF-5km
  • ????? 5km
  • 1979-2003
  • 2015-2039
  • 2075-2099 ???

19
???
??? (??) 7?-10? ???? gt 8?? ??? gt 2.5 mm/hr
?? ??????(??) ?????
??(1979-2003) - 3.04
1979-2003 3.52 3.39
2015-2039 3.24 3.39
2075-2099 3.28 3.32
Gauges
MRI-WRF ??
???
???
20
????
??? (??) 7?-10? ???? gt 8?? ??? gt 2.5 mm/hr
Gauges
MRI-WRF ??
???
???
21
??????
??? (??) 7?-10? ???? gt 8?? ??? gt 2.5 mm/hr
Gauges
MRI-WRF ??
???
???
22
????
??? (??) 7?-10? ???? gt 8?? ??? gt 2.5 mm/hr
23
???
??? (??) 11???4? ???? gt 4?? ??? gt 0.5 mm/hr
?? ?????
??(1979-2003) 7.58
1979-2003 6.94
2015-2039 7.15
2075-2099 8.37
????gt4hrs ???gt0.5mm
????gt4hrs ???gt2mm
Gauges
MRI-WRF ??
???
???
24
????
??? (??) 11???4? ???? gt 4?? ??? gt 0.5 mm/hr
????gt4hrs ???gt0.5mm
????gt4hrs ???gt2mm
Gauges
MRI-WRF ??
???
???
25
??????
??? (??) 11???4? ???? gt 4?? ??? gt 0.5 mm/hr
????gt4hrs ???gt0.5mm
????gt4hrs ???gt2mm
Gauges
MRI-WRF ??
???
???
26
????
??? (??) 11???4? ???? gt 4?? ??? gt 0.5 mm/hr
????gt4hrs ???gt0.5mm
????gt4hrs ???gt2mm
27
???
??? (??) 5?-6? ???? gt 3?? ??? gt 0.5 mm/hr
?? ?????
??(1979-2003) 6.51
1979-2003 7.16
2015-2039 6.89
2075-2099 7.55
????gt3hrs ???gt0.5mm
????gt3hrs ???gt2mm
Gauges
MRI-WRF ??
???
???
28
????
??? (??) 5?-6? ???? gt 3?? ??? gt 0.5 mm/hr
????gt3hrs ???gt0.5mm
????gt3hrs ???gt2mm
Gauges
MRI-WRF ??
???
???
29
??????
??? (??) 5?-6? ???? gt 3?? ??? gt 0.5 mm/hr
????gt3hrs ???gt0.5mm
????gt3hrs ???gt2mm
Gauges
MRI-WRF ??
???
???
30
????
??? (??) 5?-6? ???? gt 3?? ??? gt 0.5 mm/hr
????gt3hrs ???gt0.5mm
????gt3hrs ???gt2mm
31
???
??? (??) 7?-10? 3??lt???? 8?? ??? gt 2.5 mm/hr
?? ?????
??(1979-2003) 3.10
1979-2003 6.75
2015-2039 6.51
2075-2099 6.36
Gauges
MRI-WRF ??
???
???
32
????
??? (??) 7?-10? 3??lt???? 8?? ??? gt 2.5 mm/hr
Gauges
MRI-WRF ??
???
???
33
??????
??? (??) 7?-10? 3??lt???? 8?? ??? gt 2.5 mm/hr
Gauges
MRI-WRF ??
???
???
34
????
35
??
  • MRI-WRF-5km?????????????????????????????????,?????
    ??????????

36
??????
  • ?MRI-WRF-5km??????????????MRI-WRF-5km??????
  • ??MRI-WRF-5km??????????????
  • ????????????????

37
Stochastic storm rainfall simulation model (SSRSM)
  • Occurrences of storm events and time distribution
    of the event-total rainfalls are random in
    nature.
  • Physical parameters based
  • of events in a certain period
  • Duration
  • Event-total depths
  • Time distribution (hyetograph)
  • Rainfall intermittence

38
Modeling occuerrences of storms
  • Number of storm events in a certain period
  • Occurrences of rare events like typhoons can be
    modeled by the Poisson process.
  • Inter-event-time has an exponential distribution.
  • Occurrences of other types of storms which are
    more frequently occurred may not be well
    characterized by the Poisson process.

39
Duration and total depth
  • Generally speaking, storms of longer durations
    draw higher amount of total rainfalls.
  • Event-total rainfall (D) and duration (tr) are
    correlated and can be modeled by a joint
    distribution.
  • (D, tr) of typhoons are modeled by a bivariate
    gamma distribution.
  • Bivariate distribution of different families of
    marginal densities may be possible.

40
Simulation of bivariate gamma distribution A
frequency factor based approach
  • Transforming a bivariate gamma distribution to a
    corresponding bivariate standard normal
    distribution.
  • Conversion of BVG correlation and BVN correlation.

41
Gamma density
42
Rationale of BVG simulation using frequency factor
  • From the view point of random number generation,
    the frequency factor can be considered as a
    random variable K, and KT is a value of K with
    exceedence probability 1/T.
  • Frequency factor of the Pearson type III
    distribution can be approximated by

Standard normal deviate
A
43
(No Transcript)
44
  • Assume two gamma random variables X and Y are
    jointly distributed.
  • The two random variables are respectively
    associated with their frequency factors KX and KY
    .
  • Equation (A) indicates that the frequency factor
    KX of a random variable X with gamma density is
    approximated by a function of the standard normal
    deviate and the coefficient of skewness of the
    gamma density.

45
Flowchart of BVG simulation (1/2)
46
Flowchart of BVG simulation (2/2)
47
(No Transcript)
48
Time distribution of event-total rainfall
  • The duration is divided into n intervals of equal
    length. Each interval is associated with a
    rainfall percentage.
  • Based on the simple scaling assumption, rainfall
    percentages of the i-th interval (i 1, , n) of
    all events (of the same storm type) form a random
    sample of a common distribution.
  • Rainfall percentages of individual intervals form
    a random process.
  • Gamma-Markov process

49
Modeling the dimensionless hyetograph
  • Rainfall percentages can only assume values
    between 0 and 100.
  • The sum of all rainfall percentages should equal
    100.
  • Constrained gamma-Markov simulation
  • Gamma distribution will generate random numbers
    exceeding 100.
  • Truncated gamma distribution (truncated from
    above)
  • The truncation threshold (cut off value) is
    significantly lower than 100.

50
  • Observations of rainfall percentages are samples
    of truncated gamma distributions.
  • Determining parameters of the truncated gamma
    distributions.
  • Scale parameter, shape parameter and the
    truncation threshold.
  • Gamma-Markov simulation is based on simulation of
    a bivariate truncated-gamma distribution.
  • Determing the correlation coefficient of the
    parent bivariate gamma distribution.
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