Professor Ke-Sheng Cheng (Email: rslab@ntu.edu.tw)
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University
(1) Extreme rainfall data
Event-max rainfall data in Taiwan
24-hour annual-max rainfall data in Taiwan
(2) Streamflow data
40-year (1964 - 2003) flow data at the Xia-Yun station (Xia_Yuan_DailyFlow.csv)
(3) Reservoir inflow data
(4) Hourly rainfalls of storm events (hourly_rainfall_depths.xlsx) (Hourly typhoon rainfall data at Bamboo Lake, BambooLake_Typhoon_Rainfall.xslx)
(5) Daily rainfall data
Random Number Generation in R
Pseudo Random Number Generator (PRNG)
Probability Integral Transformation
Acceptance/Rejection Method
Frequency-factor-based Method
PPT - 03062017 [Updated on March 22, 2017]
General concept
General equation for frequency analysis
Data series for frequency analysis
Parameter estimation
Techniques for goodness-of-fit test
Selection of best-fit distribution
IDF curve fitting
Goodness-of-fit test using moment ratios diagram
L-moments and L-moment ratios diagram (LMRD)
Establishing acceptance region for L-moment ratios
References
R code for LMRD-GOF plotting (LMRD-GOF_Plotting.R)
Annual maximum events
Simple scaling and multiple scaling
IDF Curves and the Scaling Property
Theoretical Basis for Usage of Dimensionless Hyetographs
Simple scaling DDF (skipped)
Multiple scaling DDF (skipped)
Fundamental concept of regional frequency analysis
The index-flood approach
General procedures of regional frequency analysis
Situations for application of RFA
Regional frequency analysis with presence of extraordinary rainfalls
Alternating block hyetograph
Average rank hyetograph
Simple scaling Gauss-Markov hyetograph (SSGM)
Bivariate normal distribution
Bivariate gamma distribution
Working problems - WP-8
Random (stochastic) process
Characterizing a random process
Stationary random process
Equality of random processes
Stochastic convergence
Ergodic theorem
Examples of stochastic processes
Autogressiove model - general form
Characteristics of AR(1) and AR(2) models
Time series modeling in R (A good reference book: Time Series Analysis and Its Applications With R Examples by RH Shumway and DS Stoffer. Springer)
AR(1), Gauss-Markov process, and bivariate normal distribution
Stream flow series modeling
Working problems - WP-9 [Uploaded May 31, 2017] (R code for WP-9)
Flow persistence and the Hurst phenomenon
Flow duration curve
PPT
Stream flow series
10-day-period (TDP) rainfall series
Standardized Precipitation Index (SPI) for drought monitoring, early warning, and forecasting
PPT - Gamma Random Field Simulation
Characterizing a random field
Sequential Gaussian Random Field Simulation (SGS)
Gamma random field simulation
Potential applications
PPT - Evaluation of hydrological model performance considering uncertainties
Sources of uncertainty and uncertainty in model performance
Persistence in flood flow series
Criteria for model performance evaluation (MPE)
Coefficient of efficiency (CE), coefficient of persistence (CP), and bench coefficient
Theorectial asymptotic relationship between CE and CP
Misuse of CE and CP for MPE of realtime flood forecasting
Theoretical CE-CP relationships of the AR(1) and AR(2) models
Demonstration of MPE using model-based bootstrap samples
RSLAB - NTU
Prof. Ke-Sheng Cheng
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University