Hydrology II
kärtchen für hydro II
kärtchen für hydro II
Kartei Details
Karten | 58 |
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Sprache | English |
Kategorie | Naturkunde |
Stufe | Grundschule |
Erstellt / Aktualisiert | 15.10.2013 / 16.01.2017 |
Weblink |
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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.
how to monitor?
traditional: point measurements
- loss of information with respect to space variability
advanced: distributed in time and space
- remote sensing
how to monitor?
- traditional: point measurements
- loss of information with respect to spacial (and temporal) variability
- advanced: measurements distributed in time and space
- remote sensing
measurements are possible for:
- rainfall
- soil moisture
- vegetation cover
- land use
- thematic maps
problems of measurements by remote sensing:
low penetration of the ground: ca 5-10 cm
what does radar mean?
RAdio Detecting And Ranging
how does a weather radar work?
- emission and reception of el mag waves
- backscattering from objects
- reflectivity (Z)
- Z is dependent on Drop Seize Distribution (DSD)
- Z is converted to precipitation depth
why monitoring?
- to avoid inaccurate forecasting (due to inaccurate data base)
- understand processes
- build models
- verify modeling results
advanced methods in hydrology can....
- reproduce nature's behavior
- maximise the efficiency of planning
- minimise the risk of failure
- analyse interaction with other systems
what can the doppler effect be used for in remote sensing?
- detect moving weather systems
- estimates of 3D wind velocities
radar equation
P = (CLZ)/r2
P: Backscattered Power, measured
C: Radar constant, data
L: fractal signal loss, measured
Z: radar reflectifity factor
r: range, data
Errors in remote sensing
ground clutter
erroneous echos (birds, airplanes)
shielding
ice, snow, rain
rainfall estimation with radar:
R = aZb
R: rainfall rate
a,b: parameters, event dependent
Z: reflectivity
why dense or sparse raingauge network?
- dependent on special var of process
- target of monitoring
- installation & maintenance costs
dense: short time, high var -> thunderstorms
sparse: long time scales, lim var -> general storms, annual average
problems of raingauges
bias: underestimation due to snow, ice
precision: random uncertainities due to the sparseness of network
criteria for raingauge network design
conventional: stations/km2
optimisation methods: loss/gain criteria, bayes theorem
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