1

Modelling Runoff with Satellite Data

Nyandwaro Gilbert Nyageikaro

Patrick Willems

Joel Kibiiy

2

Outline

• Background information

• SWAT model development

• The model sensitivity analysis and

calibration

• Calibration of satellite weather data

• Conclusions

3

• Both floods and droughts have negative effects…

• Floods cause destruction of property and

displacement of people.

• Hydrological modeling can help mitigate these

effects.

• Lack of sufficient data is a challenge

• Aim: to develop a calibration technique for

satellite-based weather data.

• Research based on nzoia basin (about 12,800km2)Western kenya.

Background Information

4

Background Information…

35°0'0"E

35°0'0"E

34°0'0"E

34°0'0"E

1°0'0"N 1°0'0"N

0°0'0" 0°0'0"

5

• Basin part of Lake Victoria of the larger Nile basin

• Located mainly in agricultural zone.

• Basin is mainly tropical humid.

• Mean annual rainfall varies from about 1070 mm to

2200 mm

• Mean monthly rainfall trend of two maxima over the

year: April-May and July-November respectively

• Altitudes: 4000m-1000m a.s.l

Background Information…

6

Hydrological challenges facing Nzoia basin:

– Flooding in the lower reach (popularly known as Budalangi area)

– Floods almost on annual Occurrence

– Breaching of dykes – old and poorly maintained

– Deaths and displacement of people; Destruction of property.

Background Information…

7

Background Information…

35°0'0"E

35°0'0"E

34°0'0"E

34°0'0"E

1°0'0"N 1°0'0"N

0°0'0" 0°0'0"

Floodplain

8

“Budalang'i residents edgy as river Nzoia breaks banks”

“People are worried because of the volumes of water which have already burst the River banks. Floods are likely to occur if the water overpowers the dykes,” ……

[Daily Nation, April 14, 2013]

Background Information…

9

2008 Flood: Destruction of property 2006 Flood: Damaged dyke

2006 Flood: Destruction of Property

2006 Flood: Displaced people

Fig.: Flooding at the Lower Reaches of Nzoia

10

2003 Flood

2008 Flood

2006 Flood: Destruction of Property

2006 Flood: Displacement of People

Fig.: Flooding at the Lower Reaches of Nzoia

11

Repair of the broken dyke in Budalang'i

12During a Survey to a breached dyke in Jan 2012

13

– Weak disaster management capacity

• Facilities

• Information

• Manpower

• Funding

– Lack of sufficient data

– Aim: To develop a calibration technique for satellite-based weather data.

Background Information…

14

• Development of Rainfall-Runoff model-SWAT

model for Nzoia basin.

• Model sensitivity analyses and calibration

• Calibration of satellite weather data

Methodology

15

• Watershed: 12, 800 sq.km.

• 27 Subbasins.

• 211 HRUs- Landuse, soil and slope

• 13 Rainfall stations

• Simulation: 1974-1984

Development of Nzoia SWAT Model

16

Development of Nzoia SWAT Model

Calibration Station

17

Sensitivity analysis

• Most sensitive parameters for calibration

• LH procedure, sensitivity of parameters dependent on interval used

• Rank….groups (sensitive and non-sensitive)

• Some parameters low in ranking….crucial (GW_DELAY)…judgement needed!

• Sensitive parameters: CN2, ESCO, Sol_AWC, Surlag, GWQMN, GW_REVAP, ALPHA_BF… and GW_DELAY

18

• Based on observed discharge at downstream station.

• Models runs for the period 1974-1984.

– Warm-up: 1974-1975

– Calibration: 1980-1984

– Validation: 1976-1979

• The models performance assessed through:

– NSE

– Validation of Maxima and Minima (Box-Cox transformations)…….

Model Calibration

[Box and Cox, 1964]

19

• Water Engineering Time Series PROcessing

tool (WETSPRO) for calibration (Willems,

2008).

• Multi-criteria objecive employed.

NSE

• NSE: 0.89 Calibration; 0.81 Validation

Model Calibration

20

Model Calibration…

0

50

100

150

200

250

300

350

400

450

29000 29500 30000 30500 31000 31500Number of time steps

OBS

Filtered baseflow

SWAT

General agreement:

21

Model Calibration…Maxima (peaks):

4

6

8

10

12

14

16

4 6 8 10 12 14 16

BC

( S

imu

late

d m

axim

a )

BC( Observed maxima )

SWAT

bisector

mean deviation

standard deviation

standard deviation

22

Model Calibration…

Cumulative Vol:

0

50000

100000

150000

200000

250000

0 500 1000 1500 2000

Cu

mu

lative d

ischarg

e

Time

OBS SWAT

23

Model Calibration…Extreme maxima:

0

50

100

150

200

250

300

350

400

450

0.1 1 10

Dis

ch

arg

e

Return period [years]

OBS SWAT

24

Model Calibration…Extreme minima:

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.1 1 10

1 / D

isch

arg

e

Return period [years]

OBS SWAT

25

• USGS RFE Satellite rainfall estimates for

calibration against raingause data.

• Resolution: 8km

• Satellite data extracted at 13 points (at

raingauge stations)

• Calibration: Quartile-pertubation approach at

individual stations

• Period: 2001-2008

– 2001-2004: Calibration of Satellite data

– 2005-2008: Validation

• Analysis: Mass curves Runoff extremes.

Calibration of satellite data

26

• USGS RFE Satellite rainfall estimates for

calibration against raingause data.

• Satellite data extracted at 13 points (at

raingauge stations)

• Calibration: Quartile-pertubation approach at

individual stations

• Analysis: Runoff extremes.

Calibration of satellite data

27

Calibration of satellite data…

0

100

200

300

400

500

600

0.1 1 10

Dis

ch

arg

e

Return period [years]

OBS

UNCALIB_RFE

CALB_RFE

28

• Satellite data can be valuable where observed

data is insufficient

• Calibration to local scale can better improve

runoff estimation with satellite data

• Effect of using both satellite rainfall and pet data

to be studied

Calibration of satellite data

29

THANK YOU

Suggestions and Questions