Hydrology II
kärtchen für hydro II
kärtchen für hydro II
Kartei Details
| Karten | 58 |
|---|---|
| Sprache | English |
| Kategorie | Naturkunde |
| Stufe | Grundschule |
| Erstellt / Aktualisiert | 15.10.2013 / 16.01.2017 |
| Weblink |
https://card2brain.ch/box/hydrology_ii
|
| Einbinden |
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basics of LAM model
solve theremo- and fluodynamis equasions of the atmosphere,
born for weather forecasting are used for QPF (quantitative precipitation forecast) and for climatological studies
basics of stochastic space-timemodels
based on (non-linear) stochastic processes,
provide a tool for longterm simulations of spatio-temporal rainfall,
thus helping to solve lack of data
basics of statistical models
based on probabilistic interpretation of observed data,
especially used to describe the frequency characterization of reinfall extremes (DDF)
DDF = depth duration frequency curves
why continuous space-time rainfall modeling?
research: investigation of rainfall processes, adequate representation of hydrologic processes over a range of scales, climatic and non-stationary analysis
application: lack of data, insufficient length of record, sensitivity analysis, long term simulation, substitution of design hyetograph
options for rainfall modelling
physically based vs statistical
temproal vs space-time
event based vs continuous
simulation vs forecasting
difference between prediction and forecasting
forecasting = real time prediction
prediction = simulation, frequency etc ???
why stochastical models comared o LAMs?
efficient long term generation
robust models in all seasons and across a range of scales
convenient framework for analytical formulation of downscaling
why stochastic models as integration of LAMs?
combined use in real time prediction of rainfall
3 rainfall stochastic modelling approaches and its basic assuptions
markov theory: modelling persistence and periodicity
point process theory: modelling random ocurence in time, reproducing clustering of cells
fractal theory: modelling rainfall process through preservation of its scaling properties
whats the underleying process of storm occurrences?
the poison process
why is the point process theory a good choice for rainfall modeling?
its advantages?
rainfall is a random sequence of occurences in time (and space). the point process theory can model sequences of random occurences
advantages: analytical flexibility, cluster dependence, time and space domain analysis
independent rectangular pulses model:
formula for poisson process
parameters
advantages/ disadvantages
Poisson process: P[N(0,t)=n]=((lambda*t)n*exp(-lambda*t))/n!
parameters: lambda: mean poisson arrival time
µ: mean intensity of a pulse, exp distributed
delta: mean duration of a pulse, exp distributed
advantage: analytically simple, analytical expression of DDFs
disadvantages: poorly representative
Neyman Scott Rectangular Pulses Model (NSRP):
parameters?
differences to independent rec pulses model?
Parameters: lambda: mean poisson arrival time, mu: mean intensity of a call, delta: meand duration of a cell, beta: mean displacement of a cell from the cluster origin, nu: mean number of cells per cluster
model is more realistic than indep. rec. pulses model, but it underestimates short events.
NSRP: data requirements?
parameter estimation?
sub-daily historical series
method of moments or max. likelihood
NSRP: validation
historical vs simulated storm profiles
historical vs simulated statistics
historical vs simulated extremes
internal storm properties: scaling, prob. funct, power sp...
--> use other timescales than used for calibration
NSRP: how to solve problem of extremes?
use seasonal parameters (e.g. monthly) -> much better representation of extremes
difference of NSRP and Bartlett-lewis model?
parameter beta!
NSRP measures from the origin of the event
Bartlett-lewis measures between two successive cells
what is the generalized NSRP model?
includes 2 types of rainfalls: stratiform and convective
solves the "overlappping problem" of stratiform and convective cells.
