Impact of Point Rainfall Data Uncertainties on SWAT Simulations - - PowerPoint PPT Presentation

4th International SWAT Conference 2007 4.- 6. July UNESCO-IHP University, Delft The Netherlands

Impact of Point Rainfall Data Uncertainties on SWAT Simulations

Michael Rode & Gerald Wenk

Department of Hydrological Modelling Magdeburg, Germany

Seite 2

Motivation

• Input data uncertainties are increasingly recognised
• Rainfall data are most important

input data for rainfall runoff models

• Point rainfall data are associated

with systematic and random errors

Seite 3

Systematic point rainfall measurement errors

• Mean correction (%) of

the average annual precipitation total (1961/90)

• Moderate wind-sheltered

sites in Germany

Source: (WMO, 1998)

Seite 4

Mean correction (%) of precipitation in the Weiße Elster River Basin

Shelter class Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year a 31.6 33.5 26.9 18.3 12.5 10.4 10.8 10.5 12.6 15.5 21.8 26.5 18.2 b 23.3 24.5 20.3 15.1 11.2 9.8 10.0 9.5 11.5 12.7 16.8 19.8 14.6 c 17.3 17.9 15.5 12.7 10.1 8.8 9.1 8.5 10.2 11.0 13.3 15.0 12.0 d 11.5 11.8 10.7 10.0 8.6 7.7 8.0 7.5 8.7 8.8 9.5 10.3 9.3

Time series 1961/90, according to Richter (1995)

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Objectives

• Investigate the impact of systematic and random

rainfall point measurement errors

• Assess simulated discharge and nitrogen with SWAT
• Analyse scaling effects of these errors

Seite 6

The Weisse Elster Basin: Study area

Overview

Catchment area: 5360 km² River length: 253 km Mean discharge: 25.2 m3/s Rainfall gauge stations: 49 Discharge gauge stations: 18

Land use

Agricultural land: 62% Forest: 23% Urban areas: 13%

Seite 7

Methodological approach

• Add randomly generated correctionvalues

to uncorrected rainfall measurement values

• Assume Gumbel error distribution of PDFs
• Standard deviations of PDFs are defined

by the correction factor

• Generate 200 time series for each rainfall

gauge station (DUE)

• Compare SWAT simulations with respect

to variables and scales

Seite 8

Data Uncertainty Engine (DUE)

• Characterisation and

assessment of uncertainty in data

• Generates time series

including systematic and random errors

• Monte Carlo based

approach using pdf’s

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Calibration discharge gauge stations

• Adorf

Straßberg Läwitz Greiz Gößnitz Gera-Langenberg Zeitz Kleindalzig Böhlen Thekla Oberthau Restgebiet 1 2 3 4 5 K i l

• m

e t e r s

N C a t c h m e n t s

• f

D i s c h a r g e G a u g e s i n t h e W e i s s e E l s t e r R i v e r B a s i n u s e d f

• r

S W A T

• C

a l i b r a t i

• n

Catchment of Discharge Gauge

Adorf Böhlen Gera-Langenberg Greiz Gößnitz Kleindalzig Läwitz Oberthau Restgebiet Straßberg Thekla Zeitz Rivers Discharge Gauge

• Water Quality Gauge

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SWAT calibration discharge gauge station Läwitz (98 km²)

01.11.1996 01.03.1997 01.07.1997 01.11.1997 01.03.1998 01.07.1998 1 2 3 4 5 6 7 Discharge Läwitz (obs.) Discharge Läwitz (sim.) Discharge [m³/s]

• Reasonable

calibration results

• Problems to

represent the discharge dynamics

Seite 11

SWAT calibration discharge gauge station Zeitz (2504 km²)

01.11.1997 01.05.1998 01.11.1998 01.05.1999 01.11.1999 01.05.2000 20 40 60 80 100 120 140 Discharge Zeitz (obs.) Discharge Zeitz (sim.) Discharge [m³/s]

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SWAT calibration of DIN load, gauge station Gera-Langenberg (2186 km²)

Gera-Langenberg

20 40 60 80 Oct-95 Apr-96 Oct-96 Apr-97 Oct-97 Apr-98 Oct-98

date DIN - load [t/d]

measured simulated

• Reasonable agreement

between observed and simulated DIN loads

• Slightly overestimated

nitrogen loads

• Oversimplified represen-

tation of nitrate denitrifi- cation in the aquifer

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Mean simulated discharge using four different correction factors

1990 1992 1994 1996 1998 2000 25 50 75 100 125

• runoff [mm/month]

Mean (9,3 %) Mean (12,0 %) Mean (14,6 %) Mean (18,2 %)

• Systematic errors
• f mounthly values
• Large differences

in the case of low flows

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Mean simulated discharge using four different correction factors

1990 1992 1994 1996 1998 2000 25 50 75 100

Gera-Langenberg

runoff [mm/month] Mean (9,3 %) Mean (12,0 %) Mean (14,6 %) Mean (18,2 %)

• Comparable

differences with increasing catchment size

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Mean simulated nitrogen using four different correction factors

1990 1992 1994 1996 1998 2000 0,0 2,5 5,0 7,5 10,0 12,5 15,0

Läwitz

Total Nitrogen Load [kg/(ha*month)] Mean (9,3 %) Mean (12,0 %) Mean (14,6 %) Mean (18,2 %)

• Small effects on

simulated nitrogen loads

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Mean and maximum monthly error ranges

• f simulated discharge

10 20 30 40 50 10 20 30 40 50

Discharge [mm/month] Number of Weather Stations Mean (Range) Max (Range)

• Correction factor of

18.2%

• Randomly generated

rainfall time series

• Gumbel distribution
• Decrease of errors

with increasing rainfall gauge stations

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Mean and maximum monthly error ranges

• f simulated nitrogen

10 20 30 40 50 1 2 3 4 5

Mean (Range) Max (Range) Total Nitrogen Load [kg/(ha*month)] Number of Weather Stations

• Considerable mean

errors only when using small numbers of stations

• Maximum errors in

single month can still be significant

Seite 18

Conclusions

• Systematic rainfall measurement errors can have

considerable impact on simulated discharge and nitrogen

• These errors can be increased by random rainfall

errors

• Effect of random error rapidly decreases with

increase rainfall stations

• Nitrogen load calculations are much less sensitive

to random precipitation errors than simulated discharge