Global rivers that will be observed by SWOT and their characteristics

K. Andreadis1, E. Clark1, M. Durand2, S. Biancamaria3, E. Rodriguez4, D. Lettenmaier1, D.

Alsdorf2

SWOT Hydrology Workshop9/15/2008, Columbus, OH

1University of Washington 2Ohio State University3LEGOS 4NASA JPL

Motivation

● Obvious question on what rivers will SWOT be able to “see”

– What are their characteristics?– How many times will they be observed per

orbit cycle?

● What are the expected errors in discharge estimation?

– How far upstream the drainage network can we estimate discharge with reasonable accuracy?

● Availability of global measurements of river discharge, width, depth etc?

General approach● Power law relationships for river width and

depth (functions of discharge)● Regional regression of mean annual

discharge with drainage area

Moody and Troutman, 2002

● In-situ discharge measurements and global river dataset are required

● Estimate potential discharge errors from simple stage-discharge relationship

Datasets● HYDRO1K

– Topographically derived dataset based on GTOPO30

– Aspect, fow direction/accumulation, stream lines, drainage basins

● GRDC (Global Runof Data Center)– 3,035 stations worldwide– Mean monthly discharges

● HYDROSHEDS– Based on SRTM at 3-arcsec resolution– Hydrologically conditioned dataset

Caveats

● Major limitations with existing datasets and power law relationships

● Peak versus mean annual fow● Not looking at temporal variability here● However, we need a consistent approach

as a “frst-cut” for evaluating the potential for SWOT observations

● We need a realistic dataset of river characteristics

Algorithm

● Q: discharge● A: drainage

area● W: width● D: depth

● σz: height error

● σQ: discharge error

SWOT orbital coverage

● 22-day repeat cycle

● 78-degree inclination

● Australia from HYDROSHEDS, rest from HYDRO1K

Global river discharge

Mean annual river discharge (m3/s)

Global river width

Derived river width (m)

Global river depth

Derived river depth (m)

Sanity check for derived characteristics

● Maximum river widths and mean annual discharge

● North America: Q=13600 m3/s, W=845 m● South America: Q=145000 m3/s, W=2740

m● Africa: Q=32000 m3/s, W=1290 m● Asia: Q=17400 m3/s, W=950 m● Europe: Q=19900 m3/s, W=1020 m● Oceania: Q=12450 m3/s, W=800 m

What kind of rivers would SWOT see?

1280 km2 2430 km2 5700 km2 21000 km2 5880700 km2

Drainage Area

1

2

3-4

5-9

>10

SWOT Observations

per 22-day orbit cycle

Per

cent

age

of r

iver

s be

long

ing

in b

in

26 m 35 m 53 m 88 m 2740 m

River Width

What kind of rivers would SWOT see?

1

Strahler Order

1

2

3-4

5-9

>10

SWOT Observations

per 22-day orbit cycle

Per

cent

age

of r

iver

s be

long

ing

in b

in

2 3 4 5 6

0.00001 0.0004 0.0011 0.0026 0.485

Gradient (Δh/ΔL)

Calculating the expected error in river discharge

● We're using a simple stage-discharge relationship

● Need to take into account: i) measurement error and ii) model error (discharge)

QQ

=2 b z

z 2

– Assuming model and measurement errors are independent

Q=azb Q=QQ=∂Q∂ z

zQ

Discharge fractional error

Amazon watershed (Branco River)● Small domain in the

Amazon basin

● Example validation of methods

● River width in-situ measurements

Amazon watershed maps

● We can evaluate what order streams SWOT will observe (discharge) depending on required accuracy

Dis

char

ge (

m3/s

)

Riv

er w

idth

(m

)

Riv

er d

epth

(m

)M

onth

ly σ

Q/Q

σQ/Q

Global river discharge errors

Discharge fractional error σQ/Q

Discharge errors with river characteristics

Discharge (m3/s) Width (m)

Strahler OrderDepth (m)

σQ/Q

σQ/Q

N. America

S. America

Africa

Asia

Europe

Oceania

Australia

Discharge errors with river characteristics

Discharge (m3/s) Width (m)

Depth (m)

σQ/Q

σQ/Q

N. America

S. America

Africa

Asia

Europe

Oceania

Australia

Global river discharge monthly errors● Based on mean Q relationship ft from

gauge measurements

Future work

● Swapping HYDRO1K with more accurate HYDROSHEDS dataset

● Improve discharge estimates by using additional in-situ datasets

● Perform analysis regionally on basins where in-situ width, depth etc measurements are available

● Incorporate other models of stage-discharge and other techniques of estimating discharge

Questions?