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April 21, 2023
1
Today’s topicsDistributed modelling
08:45 – 09:30 Distributed catchment modelling
09:45 – 10:30 Choices in degree of distribution and data
Hope to give some relevant examples
April 21, 2023 2
Choice of degree of distribution
How to choose the spatial representation of your model?
• Data availability (spatial distributed data?)• Which processes are you interested in?• Computational time
• Choose between lumped parameters or distributed parameters (equifinality)
April 21, 2023 3
April 21, 2023 4
April 21, 2023 5
Equifinality
• How can you justify a lot more parameters??
0.79
0.8
0.81
0.82
0.83
0.84
0.85
0.86
200 220 240 260 280 300
FC [mm]N
S c
oef
fici
ent [-] m
0.79
0.8
0.81
0.82
0.83
0.84
0.85
0.86
0 0.2 0.4 0.6 0.8 1
Perc [mm]
NS
coef
fici
ent [-] m
April 21, 2023 6
April 21, 2023 7
Other data sources
Eobs
CR(FC)Pn
EsimConstraining on
evaporationConstraining on
evaporation
IP
T(FC,L)Pn
FCR
April 21, 2023 8
Methodology
HighlandsForested
Dambos (wetlands)Riverine
April 21, 2023 9
Results
April 21, 2023 10
Application e.g. flood forecasting
April 21, 2023 11
Example of distributed responses
April 21, 2023 12
Application e.g. flood forecasting
April 21, 2023 13
Data sources
April 21, 2023 14
Introduction
• History: from point to grid geo-statistical interpolation, e.g.• Thiessen polygons (nearest neighbour)• Kriging (co-variance matrix approach)• Inverse distance weighted• See also: lecture notes hydrological
measurements• General problem: by interpolating, you loose
(local) extremes
April 21, 2023 15
Introduction
• Now:• Remote sensors on satellites provide new data:• …to help estimating parameters e.g…
April 21, 2023 16
Elevation
• Slopes• Drain direction• Catchment delineation• Wetland and lake identification
April 21, 2023 17
Land cover
• Root zone depth• Hydrotope
delineation• Estimate of
interception capacity
• Often, links are made with extensive lookup tables (e.g. SWAT, SOBEK RR)
Interception
Uns
atur
ated
zon
eG
roun
dwat
er
Rainfall Radiation, humidity /etc.
Base flow
(Sub)surface flowTranspiration
(Sub)surface flow
1-αα
Base flow
Transpiration
RainfallRadiation, humidity /etc.
Interception
Flux
State
PercolationPercolation
Perception Model structure
River discharge
Interception
Uns
atur
ated
zon
eG
roun
dwat
er
Rainfall Radiation, humidity /etc.
Base flow
(Sub)surface flowTranspiration
(Sub)surface flow
1-αα
Base flow
Transpiration
RainfallRadiation, humidity /etc.
Interception
Flux
State
PercolationPercolation
Perception Model structure
River discharge
Distributed model ‘wflow’
• Uses terrain analysis (derivation of flow direction, slopes, streams)
• Uses lookup tables to link model parameters with soil types, land cover classes
04/21/23 19
April 21, 2023 20
• Remote sensors on satellites provide new data:• …to help estimating parameters e.g…• …to help estimating temporally and spatially
distributed data e.g…
April 21, 2023 21
Rainfall
• Generally based on a combination of information from different sensors
• Many rainfall products available• Tropical Rainfall Measuring Mission (TRMM,
~25x25 km, 3-hourly)• GSMaP (~10x10 km, 1-hourly)• FEWS RFE 2.0 (10x10 km, daily)• PERSIANN CCS (4x4 km, 30-min!!)
Validation and bias-correction is often required!!!
April 21, 2023 25
Energy budgets
• E.g. incoming solar radiation at the land surface• Provides a strong indicator for the
evaporative potential• For Europe and Africa, LSA SAF products (see
http://landsaf.meteo.pt)
April 21, 2023 26
Energy budgets
1n solar long longR R R R
April 21, 2023 27
Summarizing: application of remote sensing
• Provide input (e.g. rainfall, (potential) evaporation)
• May be used to constrain model structures and parameters
• Mitigating the ‘equifinality problem’ by incorporating the spatially distributed data in a performance criterium