BSEN 5520 SWAT Lab 2 Courtney Harkness 6 April 2015

BSEN 5520

SWAT Lab 2

Courtney Harkness

6 April 2015

Abstract: This exercise taught the importance of sensitivity analysis. A watershed was

created, and a sensitivity analysis was performed in order to determine the most valuable

parameters. Ten parameters were calibrated to find the four most sensitive. Learning how

to properly implement a sensitivity analysis is vital to properly understanding

hydrological characteristics of different watersheds.

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INTRODUCTION

SWAT, or Soil Water Assessment Tool, is a sophisticated model used for any

hydrological analyses. The program contains numerous tools to predict or model

processes within a watershed. This water quality assessment tool has the capability to

assess a wide variety of watershed management problems. It can take a watershed and

divide it into several hundred sub-basins at a time, otherwise known as hydrologic

response units (HRUs). Auto-fertilization and auto-irrigation management options,

canopy storage of water, CO2 component added to crop growth model for climate change

studies, Penman-Monteith potential evapotranspiration equation, later flow of water in

the soil based on kinematic storage mode, and in-stream nutrient water quality equations

are few of the advanced tools available in SWAT.

This lab it setting up an ArcSWAT model, running it, extracting several output

files (as learned in the previous lab) and performing a sensitivity analysis. The sensitivity

analysis is to establish the basis for model calibration. Greensboro Watershed is the

location used for this lab exercise with an area of 294 km2. The figure below portrays the

location and classes of land use for the watershed area (Figure 1). SWAT is used to

determine the impacts of land use and land change on water quality of Greensboro. In

doing so, calibration is a vital process when trying to bring the model results closer to

observations. Performing a sensitivity analysis helps to identify which parameters are

most sensitive. Once those parameters are determined, calibration can be used solely on

those sensitive parameters without altering model performance.

Figure 1. SWAT land use and classification of soils for Greensboro Watershed.

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METHODS

To start SWAT, a new empty map was opened within ArcMap. All the SWAT

extensions were turned on and a new ArcSWAT project was set up. Several steps were

then taken to process the elevation dataset. Within the Watershed Delineation tool, the

DEM setup, Stream Definition, and Flow direction and accumulation were used. Streams

and outlet were also created and placed in the watershed. Once a watershed was

delineated, sub-basin parameters were also calculated.The HRU analysis tools were for

the Land Use/Land Cover data. Dataset grid files were downloaded, along with the

SWAT Land Use Classification Table, and displayed on the map. From the ArcSWAT

STATSGO combo box, soil code were linked to the classification table and also

displayed on the map. The slopes were reclassified in the Number of Slope classes combo

box. The HRU Definition tool was then accessed from HRU Analysis menu. Multiple

HRU’s were selected and the percentages over sub-basin area changed for the Land Use,

Soil class, and Slope class. Once the setup of HRU’s was completed, a report was

generated that displayed analysis reports. The Weather Stations tool was used to load the

weather data. For a SWAT simulation using measured weather data, weather simulation

information is needed to fill in missing data and to generate relative humidity, solar

radiation, and wind speed. The data set uses weather generator data from the US first

order stations. The rainfall data and temperature data tabs were altered. The final step of

setting up the model was creating ArcView databases and SWAT input files. The Write

Input Tables menu was used to create the ArcSWAT databases and SWAT input files

containing default settings for SWAT input. Once the SWAT input database was

initialized, SWAT was run.

What changed for Lab 2:

1. On Watershed Delineation dialog box, DEM setup, select Burn In instead of Mask and

select nhd shapefile from the shapefiles folder.

2. Land Use/Soils/Slope Definition in the HRU Analysis menu: for Land cover lookup

table select NLCD 2001 table and for soils data select “Load ArcSWAT US

STATSGO from disk”

Procedure:

1. Baseflow/surface runoff separation: separate calibration of baseflow and surface runoff

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components. Baseflow and surface runoff are not observed, rather estimated from

streamflow using filtering techniques. Run the web based baseflow separation tool at

(https://engineering.purdue.edu/~what) to separate streamflow into baseflow and surface

runoff components.

2. Identify the most sensitive parameters for surface runoff and baseflow: Identify the

processes and parameters that effect surface runoff and baseflow using the local

sensitivity analysis. How does modifying these parameters affect your simulation results?

RESULTS

1. Plot time series of surface runoff, baseflow and streamflow for the whole period

at monthly and annual time scales from Jan, 1984 to Dec, 2004 (for observed

flows).

a. What is the ratio of baseflow volume to streamflow volume?

i. Monthly = 82.45006/126.2428 = 0.6531

ii. Annually = 958.9269/1481.383 = 0.6473

b. Is this a surface or baseflow dominated system? This is a base flow

dominated system.

c. Also what fraction of your total precipitation turns into streamflow over

this period? (Surface runoff/total flow)

i. Monthly = 45.02614/126.2428 = 0.3329

ii. Yearly = 522.5003/1481.383 = 0.3527

d. Can you say anything about average ET over the same period? Since base

flow is greater than surface flow, there is not a lot of runoff. Therefore, the

average ET is determined to be fairly high over the same period.

e. How does your baseflow and surface runoff compare to SWAT generated

counterparts? The average observed flow is 126.581 cms and the average

simulated flow is 5.39, which is a ratio of 0.04258.

https://engineering.purdue.edu/~what

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0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

0 50 100 150 200 250

Simulated TF

Observed TF cms

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

0 50 100 150 200 250

Simulated GW

Observed GW cms

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0

100

200

300

400

500

600

700

800

0 50 100 150 200 250 300 350

Observed Monthly Flow

Flow (m3/s) Direct Flow (m3/s) Base Flow(m3/s)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

0 50 100 150 200 250

Simulated SR

Observed SR cms

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Time Period

Average Total Flow

Average Surface Runoff

Average Base Flow

Monthly 126.2428 45.02614 82.45006

Annually 1481.383 522.5003 958.9269

2. Identify the top four most important parameters affecting baseflow and the top

four most important parameters affecting surface runoff. Use long term averages

as the metric. Use both absolute sensitivity (AS) and relative sensitivity (RS)

measures (Refer to Fu et al., 2010).

a. Is there a difference in your list of key parameters due to the use of AS or

RS? Yes, by analyzing the use of absolute sensitivity and relative

sensitivity, the value of each parameter was revealed. For the most

sensitive parameters, the absolute sensitivity and relative sensitivity vary

slightly. Overall, each parameter returned different results. For instance,

the AS and RS did not change for the GW_Delay, Alpha_Bf, surlag, and

Revapmn. The equations below were used to find the values for each

parameter.

𝐴𝑆 = (𝑂𝑃+∆𝑃−𝑂𝑃−∆𝑃)

2∆𝑃 and 𝑅𝑆 =

[(𝑂𝑃+∆𝑃−𝑂𝑃−∆𝑃)/𝑂𝑃]

2∆𝑃/𝑃

b. Explain how each parameter affects the relevant process. The lists below

0

500

1000

1500

2000

2500

3000

3500

1980 1985 1990 1995 2000 2005 2010 2015

Observed Annual Flow

Flow (m3/s) Direct Flow (m3/s) Base Flow(m3/s)

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defines each parameter and gives a little background information as to

why it is important for hydrologic modeling.

i. CN: this is one of the most valuable parameters for any hydrologic

model. It is a function of the soils land use, antecedent soil water

conditions, and permeability. It provides the basis for the amount

of runoff based on land use and soil types. This explains why it

proved to be most sensitive.

ii. esco: soil evaporation and compensation coefficient. This

parameter is used to evaporation demand for a soil layer. As esco

is reduced, SWAT extracts more of the evaporative demand from

lower levels.

iii. Alpha_Bf: base flow recession constant. This parameter is valuable

when used in shallow aquifer calculations.

iv. Slope: this parameter can effect numerous aspects of a

hydrological model. The topographic factor, coarse fragment

factor, and USLE are a few of the many variables dependent on

slope.

v. Rechrg_Dp: aquifer percolation coefficient. This parameter is used

as an input variable for groundwater height.

vi. GW_Delay: delay time for the aquifer recharge. This parameter is

used as an input variable for groundwater height.

vii. Surlag: surface runoff lag coefficient that pertains to the lag

calculations.

viii. Revapmn: threshold water level in shallow aquifer for revap. This

parameter is used as an input variable for groundwater height.

ix. GW_Revap: revap coefficient, used as an input variable for

analyzing the groundwater height.

x. epco: plant uptake coefficient factor that ranges from 0.01 to 1.00

and is set by the user. As it nears 1.00, the water uptake demand is

met by lower layers in the soil.

Parameter Curve Number

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Changed:

Average Value 63.32

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.41 2.45 2.96

Decreased by 10% 5.39 0.80 4.59

Absolute Sensitivity 0.00 0.13 -0.13

Relative Sensitivity 0.02 4.97 -2.18

Parameter Changed: esco

Average Value 0.95

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.80 1.70 4.09

Decreased by 10% 4.92 1.60 3.33

Absolute Sensitivity 0.07 0.54 4.01

Relative Sensitivity 0.81 0.31 1.02

Parameter Changed: Alpha_Bf

Average Value 0.05

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.66 3.74

Decreased by 10% 5.39 1.66 3.74

Absolute Sensitivity 0.00 -0.01 -0.14


Parameter Changed: Slope

Average Value 1.00

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.66 3.73

Decreased by 10% 5.39 1.65 3.74



Parameter Changed: Rchrg_Dp

Average Value 0.05

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.68 3.72

Decreased by 10% 5.39 1.64 3.76

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Parameter Changed: GW_Delay

Average Value 31.00

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.66 3.74

Decreased by 10% 5.39 1.66 3.74



Parameter Changed: Surlag

Average Value 0.00

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.66 3.74

Decreased by 10% 5.39 1.66 3.74



Parameter Changed: Revapmn

Average Value 750.00

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.66 3.74

Decreased by 10% 5.39 1.66 3.74



Parameter Changed: GW_Revap

Average Value 0.02

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.37 1.66 3.72

Decreased by 10% 5.41 1.66 3.75


Relative Sensitivity -0.03 0.00 -0.05

Parameter epco

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Changed:

Average Value 1

TF SW GW

Original 5.39 1.66 3.74

Increased by 10% 5.39 1.66 3.74

Decreased by 10% 5.40 1.66 3.74



3. Provide a table showing the sensitivity rankings for baseflow, surface runoff and

total flow (based on long term averages). Provide values of AS and RS in your

table.

a. Again do you see any differences in your sensitivity orders as a result of

the use of AS and RS? The curve number and Rchrg_Dp increased the

surface runoff but decreased the groundwater for AS and RS. GW_Revap

has different results for total flow, surface runoff, and groundwater for AS

and RS. There is no change in total flow for AS but a minor decrease for

RS. Alpha_Bf does not change for the RS but decreases the surface runoff

and groundwater for the AS. The only change in surlag was a decrease in

total flow for RS. GW_Delay and Revapmn showed no change and

therefore, have no major effects on the process. The slope showed a

minimal increase for the surface runoff and a minimal decreased for the

groundwater as a result of the AS and RS. As a result of the AS, epco

decreased for surface runoff and groundwater. Yet, epco reveals no change

compared to the relative sensitivity.

Parameters AS for TF AS for SW AS for GW

CN 0.00 0.13 -0.13 epco 0.00 -0.01 -0.02 esco 0.07 0.54 4.01 GW_Revap 0.00 -0.06 -9.08 Revapmn 0.00 0.00 0.00 Alpha_Bf 0.00 -0.01 -0.14 Surlag 0.00 0.00 0.00

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Slope 0.00 0.05 -0.05 GW_Delay 0.00 0.00 0.00 Rchrg_Dp 0.00 4.08 -4.11

Parameters RS for TF RS for SW RS for GW CN 0.02 4.97 -2.18

epco 0.00 0.00 0.00 esco 0.81 0.31 1.02 GW_Revap -0.03 0.00 -0.05 Revapmn 0.00 0.00 0.00 Alpha_Bf 0.00 0.00 0.00 Surlag -0.21 0.00 0.00 Slope 0.00 0.03 -0.01 GW_Delay 0.00 0.00 0.00 Rchrg_Dp 0.00 0.12 -0.06

CONCLUSION

SWAT is a valuable tool used to perform sensitivity analysis for hydrologic models.

Numerous steps were taken to define the four most sensitive parameters based on the

watershed. The Greensboro watershed was created and used as the default map or

original watershed when running an analysis on different parameters. The manual

calibration was adjusted based on the parameter being inspected. Once the desired

parameter was chosen, it was increased by 10% or decreased by 10%. The SWAT model

was run again and the output downloaded through Access. The average base flow,

surface flow and total flow was derived and compared to the original values of the

Greensboro watershed. This is what indicated how sensitive that parameter was.

Sensitivity analysis is a crucial part to developing any useful hydrological model.

Sense hydrological conditions vary drastically between different watersheds, sensitivity

analysis are performed to reduce the uncertainty. Complex hydrological and water quality

models require parameters that cannot be measured accurately to define the model. So

sensitivity analysis is used to better define these parameters. This is critical to

understanding and improving environmental assessment.

BSEN 5520 SWAT Lab 2 Courtney Harkness 6 April 2015

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