applied hydrology climate change and hydrology

Applied Hydrology

Climate Change and HydrologyProfessor Ke-Sheng ChengDepartment of Bioenvironmental Systems EngineeringNational Taiwan University

Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

Effect of climate change on storm characteristicsStorm typesConvective stormsTyphoonsMCS (Mei-yu)Frontal systemsAssessed based on MRI high-resolution outpots (dynamic downscaling)*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


TCCIP Team 3*

*

(MRI)

(2mm/hr)(12 hours)*


*

()(1/2)() ()()(1/2)()


*MRI-WRF-5km(1979-2003)(1979-2003)MRI-WRF-5km(2015-2039)MRI-WRF-5km(2075-2099)MRI-WRF-5km


()1 0.5mm

*

()5-6 > 3 > 0.5 mm/hr()7-10 > 8 > 2.5 mm/hr()7-103 > 8 > 2.5 mm/hr()11~4 > 4 > 0.5 mm/hr


1979-200384MRI-WRF-5km: 5km1979-20032015-20392075-2099 *


*GaugesMRI-WRF

()7-10 > 8 > 2.5 mm/hr

()(1979-2003)-3.041979-20033.523.392015-20393.243.392075-20993.283.32


*GaugesMRI-WRF

()7-10 > 8 > 2.5 mm/hr


*

()7-10 > 8 > 2.5 mm/hr


*Gauges>4hrs>0.5mm>4hrs>2mmMRI-WRF

()11~4 > 4 > 0.5 mm/hr

(1979-2003)7.581979-20036.942015-20397.152075-20998.37



()11~4 > 4 > 0.5 mm/hr


*Gauges>4hrs>2mm>4hrs>0.5mmMRI-WRF

()11~4 > 4 > 0.5 mm/hr


*>4hrs>2mm>4hrs>0.5mm

()11~4 > 4 > 0.5 mm/hr


*Gauges>3hrs>0.5mmMRI-WRF >3hrs>2mm

()5-6 > 3 > 0.5 mm/hr

(1979-2003)6.511979-20037.162015-20396.892075-20997.55



()5-6 > 3 > 0.5 mm/hr


*>3hrs>0.5mm>3hrs>2mm

()5-6 > 3 > 0.5 mm/hr


*GaugesMRI-WRF

()7-103 2.5 mm/hr

(1979-2003)3.101979-20036.752015-20396.512075-20996.36


*GaugesMRI-WRF

()7-103 2.5 mm/hr


*


MRI-WRF-5km

*


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 DurationEvent-total depthsTime distribution (hyetograph)Rainfall intermittence

*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


Modeling occuerrences of stormsNumber of storm events in a certain periodOccurrences 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.*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


Duration and total depthGenerally 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. *Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


Simulation of bivariate gamma distribution A frequency factor based approachTransforming a bivariate gamma distribution to a corresponding bivariate standard normal distribution.Conversion of BVG correlation and BVN correlation.*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


Lab for Remote Sensing Hydrology and Spatial Modeling

Gamma density


Rationale of BVG simulation using frequency factorFrom 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[A]Standard normal deviate



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.


Flowchart of BVG simulation (1/2)


Flowchart of BVG simulation (2/2)


Time distribution of event-total rainfallThe 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*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


Modeling the dimensionless hyetographRainfall percentages can only assume values between 0 and 100.The sum of all rainfall percentages should equal 100%.Constrained gamma-Markov simulationGamma distribution will generate random numbers exceeding 100%.Truncated gamma distribution (truncated from above)The truncation threshold (cut off value) is significantly lower than 100%.*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


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.*Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU*


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