Evaluation of Mixed Forest Evapotranspiration and Soil ... · ... energy and carbon ... uncertainty analysis methodology could be more

KONKUK UNIVERSITY

5 August 2010

Evaluation of Mixed Forest Evapotranspiration and Soil Moisture using Measured and SWAT Simulated Results in

a Hillslope Watershed

JOH, Hyung-KyungGraduate Student

LEE, Ji-Wan / SHIN, Hyung-Jin / PARK, Geun-AeGraduate Student / Ph. D. Candidate / Ph.D.

KIM, Seong-JoonProfessor

Dept. of Civil and Environmental System Eng., Konkuk University, Seoul, South Korea

『CHUNCHEON GLOBAL WATER FORUM 2009』 Konkuk University

Contents

I. Introduction

II. Material and Methods

Study Watershed Description

Model Description

Gauging Stations

Input and Measured Data for Model Simulation

III. Results and Discussion

Sensitivity Analysis of Model Parameters

Model Calibration and Verification

IV. Summary and Conclusion

1 / 20


3

In many SWAT modeling researches, the streamflow was thesingle most commonly used watershed response variable.

However, considering numerous sources of uncertainty andthe complexity of recently developed models, the approachoften has errors to generate consistent parameter sets.

One of possible methods to reduce calibration uncertainty isto utilize of additional observation data, and this utilization canexplain the hydrological behaviors more accurately within thewatershed.

Accordingly, this study is to evaluate the SWAT model byusing measured streamflow (Q), evapotranspiration (ET)and soil moisture (SM). This evaluation is expected toimprove the ability of model predictions.

Purpose of this study

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Study procedure

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Hydrologic Cycle

http://www.bgr.bund.de/nn_335088/EN/Themen/Wasser/Bilder/Was__wasser__startseite__wasserkreis__g__en.html

Precipitation

Evapotranspiration

Runoff

Soil Moisture

Evaporation

Groundwater recharge

4 / 20


Model theory

SWt = Final soil water content (mm)SW0 = Initial soil water content on day i (mm)Rday = Amount of precipitation on day i (mm)Qsurf = Amount of surface runoff on day i (mm)Ea = Amount of evapotranspiration on day i (mm)Wseep = Amount of water entering the vadose zone from the soil profile on day i (mm)Qgw = Amount of return flow on day i (mm)

Water balance equation

The hydrology cycle as simulated by SWAT is based on the water balance equation:

5 / 20


Study Area

Watershed area : 8.54 km2

Annual average precipitation : 1,210 mm

Annual average temperature : 10.3 ℃

Seolma-Cheon watershed

<Mixed Forest>

<Sandy Loam>

<Watershed Outlet>

Forest area : 96.2 % (8.22 km2)

Soil texture : Sandy loam, Loam

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Streamflow gauge system

Gauge Stations

http://seolmacheon.kict.re.kr/main/codex/survey/page.jspx?id=discharge

In general, observation of Q is used in planning and designing water resources projects.

Q is measured by Korean Institute of Construction and Technology using ParshallFlume systems at watershed outlet.

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Gauge Stations Eddy covariance flux system

http://hsc.re.kr/main/sub02_01_04.html

ET is frequently a major component of water balance for many different types of ecosystems, and is a flux linking water, energy and carbon cycles.

The accurate estimation of water loss by ET is very important for assessing water availability and requirements, making proper water resources plans, and calibrating and improving hydrologic models.

Thus, the ET is observed by Korea Institute of Construction and Technology and Yonsei Univ. using Eddy covariance flux system, micrometeorologic observing system on the tower at mixed forest area since 2007.

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Gauge Stations Soil moisture gauge system

http://hsc.re.kr/main/sub02_01_03.html

SM conditions controls many near surface processes including land-surface-atmosphere, land surface fluxes, vegetation phenology, and soil respiration.

SM is also a very important water balance component for making water resources plans, and calibrating and improving hydrologic models.

SM is measured by Korean Institute of Construction and Technology using Time Domain Reflectometry (TDR) sensors at sandy loam and mixed forest area since 2007.

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Data set for SWAT model

Input and Measured Data

Data Type Source Scale / Periods Data Description / Properties

Terrain Korea National Geography Institute 30 m Digital Elevation Model (DEM)

Soil Korea Rural Development Administration 1/25,000

Soil classification and physical properties viz. texture, porosity, field capacity, wilting point, saturated conductivity, and soil depth

Land use 2004 Landsat TM Satellite Image 1/25,000 Landsat land use classification (8 classes)

WeatherKorea Institute of

Construction Technology / WAter Management Information System

2003-2008Daily precipitation, minimum and maximum temperature, mean wind speed and relative humidity data

Streamflow Korea Institute of Construction Technology 2003-2008 Daily streamflow data at watershed outlet

Evapotranspiration

Korea Institute of Construction Technology

/ Yonsei Univ.2007-2008 Daily evapotranspiration data at mixed forest area

Soil Moisture

Korea Institute of Construction Technology 2007-2008 Bihourly soil moisture data at mixed forest and

sandy loam area

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SWAT Input data

(b) Soil(a) DEM (c) Landuse

GIS Input Data

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SWAT Input data

GIS Input Data

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130 mm Depth15 m

Vegetation height

Evaluation ofSM

Evaluation ofET


Latin Hypercube (LH) – One-factor-At-a-Time (OAT)

A parameter SA provides insights on which parameters contribute most to the outputvariance due to input variability. In this study, we performed an LH-OAT SA.

The SA was performed for 18 parameters of hydrology that are related to Q, ET andSM.

The parameters for the calibration were selected by the SA results.

(1)

(2)

(3) (4) (5)(6) (8)(9)

(10)

Sensitivity Analysis (SA)

ALP

HA

_BF

CA

NM

X

CH

_K2

CH

_N2

CN

2

EPC

O

ESC

O

FFC

B

GW

QM

N

OV_

N

REV

APM

N

SOL_

ALB

SOL_

AW

C

SOL_

BD

SOL_

K

Surla

g

GW

_DEL

AY

GW

_REV

AP

Parameters

Mea

n Se

nsiti

vity

(1)

(2)(3)

(4) (5)(6)

(7) (8) (9)(10)

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『CHUNCHEON GLOBAL WATER FORUM 2009』 Konkuk University13 / 20

Calibrated parameters for SWAT model

Calibrated parameters

Parameters Description Range AdjustedValues

Abraham et al. (2007)

Feyereisenet al. (2007)

Q

CN2 SCS curve No. for moisture condition 35 ~ 98 70 -25 % 50

GWQMNThreshold depth of water in the shallow aquifer required for return flow to occur

-1000 ~ 1000 0 10 0

GW_DELAY Groundwater delay 0 ~ 500 100 20 1

GW_REVAP Groundwater “revap” coefficient 0.02 ~ 0.2 0.2 0.15 0.02

Surlag Surface runoff lag coefficient 1 ~ 24 24 - -

ETCANMX Maximum canopy storage 0 ~ 100 5 - -

EPCO Plant uptake compensation factor 0 ~ 1 1 - -

ESCO Soil evaporation compensation factor 0 ~ 1 0.01 0.1 0.74

SM

CANMX Maximum canopy storage 0 ~ 100 5 - -

ESCO Soil evaporation compensation factor 0 ~ 1 0.01 0.1 0.74

SOL_AWC Available water capacity of the soil layer 0 ~ 1 0.15 - -

SOL_BD Moist bulk density 0.9 ~ 2.5 1.6 - -


Discharge

Calibration and Verification

Calibration period : 2007 / Verification period : 2003-2006, 2008

Using daily streamflow records

0.0

1.0

10.0

100.0

1000.0

0.1

10000.0

Stre

amflo

w(m

m)

0

200

400

600

800

Pre

cipi

tatio

n (m

m)

VerificationPeriods

VerificationPeriods

CalibrationPeriods

200820072006200520042003

Precipitation Observed Simulated

R2=0.74, E=0.71

<Watershed Outlet>

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Discharge


Calibration period : 2007 / Verification period : 2003-2006, 2008

Using daily streamflow, evapotranspiration and soil moisture records

0.0

1.0

10.0

100.0

1000.0

0.1

10000.0

Stre

amflo

w(m

m)

0

200

400

600

800

VerificationPeriods

VerificationPeriods

CalibrationPeriods

200820072006200520042003


R2=0.77, E=0.75

Pre

cipi

tatio

n (m

m)

<Watershed Outlet>

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Evapotranspiration


Calibration period : 2007 / Verification period : 2008


Eva

potra

nspi

ratio

n(m

m)

0.0

6.0

8.0

10.0

4.0

2.0

0

100

200

400

500

VerificationPeriods

CalibrationPeriods

20082007


R2=0.52, E=0.74 Pre

cipi

tatio

n (m

m)

<Mixed Forest>

300

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Evapotranspiration




Eva

potra

nspi

ratio

n(m

m)

0.0

6.0

8.0

10.0

4.0

2.0

VerificationPeriods

CalibrationPeriods

20082007


R2=0.59, E=0.74 Pre

cipi

tatio

n (m

m)

<Mixed Forest>

0

100

200

400

500

300

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Soil moisture




Soi

l Moi

stur

e (%

)

0

200

400

600

8000.0

20.0

30.0

10.0

20082007

VerificationPeriods

CalibrationPeriods


R2=0.49, E=0.74

Pre

cipi

tatio

n (m

m)

<Sandy Loam>

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Soil moisture




Soi

l Moi

stur

e (%

)

0

200

400

600

8000.0

20.0

30.0

10.0

20082007

VerificationPeriods

CalibrationPeriods


R2=0.59, E=0.74

Pre

cipi

tatio

n (m

m)

<Sandy Loam>

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This study was tried to identify uncertainty of SWAT modelparameters by evaluating the model using measured ET and SMdata.

After all, the model results were improved when the calibrationwas conducted using measured data.

The capability of the behavior of the simulation model anduncertainty analysis methodology could be more effectivelytested if the calibration and verification could be applied toa ‘data rich’ watershed.

Summary and Conclusion

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For further information, please contact:

JOH, Hyung-KyungGraduate student, Dept. of Civil & Environmental System Engineering, Konkuk University

[email protected]

“ Thank You ”

This research was supported by a grant (code # 2-2-3) from Sustainable Water

Resources Research Center of 21st Century Frontier Research Program and by Basic

Science Research Program through the National Research Foundation of Korea (NRF)

funded by the Ministry of Education, Science and Technology (2009-0080745).

Dept. of Civil and Environmental System Eng., Konkuk University, Seoul, South Korea

Documents

Evaluation of Mixed Forest Evapotranspiration and Soil ... · ... energy and carbon ... uncertainty analysis methodology could be more