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

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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)

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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

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Other data sources

Eobs

CR(FC)Pn

EsimConstraining on

evaporationConstraining on

evaporation

IP

T(FC,L)Pn

FCR

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Methodology

HighlandsForested

Dambos (wetlands)Riverine

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Results

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Application e.g. flood forecasting

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Example of distributed responses

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Application e.g. flood forecasting

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Data sources

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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

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Introduction

• Now:• Remote sensors on satellites provide new data:• …to help estimating parameters e.g…

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Elevation

• Slopes• Drain direction• Catchment delineation• Wetland and lake identification

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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

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• Remote sensors on satellites provide new data:• …to help estimating parameters e.g…• …to help estimating temporally and spatially

distributed data e.g…

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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!!!

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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)

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Energy budgets

1n solar long longR R R R

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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