Lecture 1 Flashcards
10 confilcting needs for reservoir
drinking water irrigation flood control /drough control industry recreation ecosystems river stability hydropower commercial navigation quality
what is a hydrolgoical model
simplified math representation of hydrological process that form the cycle
difference bewteen hydrological modelling and hydraulic modelling
hydrological- applied in hydrographic basins where precipitation events are transformed into dischatrges
hydrauli involves flood propagation in river
two main model classificaitons
physical
Numerical
purpose of a hydrological model
processes that cant be observerd
predictions( forescast, scenarios)
designs
what is a state variable
set of variables that show the effect of its history in terms of its response in the future
what is a paramter
measurable characteristic , usually a constant in a single simulation. Only changed when you need to asjust model behavour
two main typoes of hydrological model
deterministic-physical processes
stochastic-based on data and porobabilities
three types of deterministic
physically based
conceptual-semi emperical -inter connected reservoirs
Observation orientated based on input and output time series
ways of breaking a domain up for deterministic modelling
LUMPED- abc model
SEMI-DISTRIBUTED-top net
DISTRIBUTED-swat
lumped explaination
-done vare for spatial variability of anything
usses averaged values
average parameters meands average processes–but because of non linearity and threshold -big errors
Distributed model explaianation
spatial variation
divided into number of eleements-runoff calculated individually and joined
three time scale classificaiton
event based
continuous
large time scales
four space scale cladssifications
small (less than 100 km2)
medium (100-1000 km2)
large (>1000 km2)
global
Physically based model advantages
advanced process description good for small scale paramaters measured in field application sot ungauged basins possible low number of calibrated parameters
Physically based model dis advantages
only small scales too simple for fast processes complecated maths material relation on linear high input data high number of parameters
conceptual model advan
simple discription processes not complicated math good large performance low inpu tdatat low number of parameters
conceptual dis ad
too simple process require calibration-optimum not unique land changes difficult ungauged difficult needs relation between parameters and catchmet characterisitics
emperical advant
simple form
simple parameter estimations
include emperical knowledege (like floods occur during snow melt)
emerpical dis
no processes included
no understanding or insight
not applicable to other places
land use and changes make not valid
difference between model and program
model-equations that rperesent behavoiur
program-code that runs solver for equations
5 things to remember when modelling
evaluate data doco assumpotions plan simualtions review output document results
inputs
storage and transfer
outputs
what are the function names fotr these
forcing function
process function
response function
three things you need for a hydrologiucal model
meterological data Basin model( connectivity etc) control spcificaitons (duration, time steps)
4 modelling components
input system characteristics governing equations initial and boundary equaiotns output
what are some sources of uncertainty
input data error ( observation error, interpolation error)
system parameters
modelling (process representation)
output measurement error
