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Performance Evaluation of SWAT with a Conceptual Rainfall-Runoff Model GR4J for a

Catchment in Upper Godavari River Basin

Aatish Anshuman, Prof. Eldho T.I. and Dr. Aiswarya Kunnath-Poovakka

Civil Engineering Department

Indian Institute of Technology Bombay

Powai, India

19-01-2018 SWAT -2018

• Introduction to Water Resources Management

• Brief Overview of eWater’s Source

• Study Objectives

• Overview of Models Used

• Study Area and Datasets• Study Area and Datasets

• Methodology

• Results and Discussion

• Conclusion

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IntroductionWater Resources Management

Planning Developing Distributing Managing the optimum use

Water Resources}Goal: Sharing in equitable basis amongst the stakeholders

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All competing Water Demands

Water Resources Assessment Hydrological Models

Planning and Management

o Model Complexity o Data Requiremento Structural Parameters

Points to consider while choosing Hydrological Models

Distributed/Semi Distributed/Semi Distributed Models

Conceptual Models Black Box Models

Neither Physically

nor, Conceptually

Based

Conceptually Based , Depends

Upon Model Structure

Complex

High

Physically Based

Over Simplified Hydrological

Cycle

Low Low

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Based on Input and Output

• Distributed models although most ideal, they are not suitable for data scarce areas.

• Conceptual models are gaining popularity due to their simple structure and less data

requirement.

• These models have very few parameters which also helps in faster model setup and

calibration.

• Conceptual models have been applied at various parts of world and proved to be very

good water resources management tools. However studies in Indian catchments are

scanty.

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• Methodology


• Conclusion

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eWater’s Source

eWater’s Source ,developed by CRC , Australia ,provides a modelling framework

for conceptual models.

The framework provides flexibility in choosing models , objective function for

calibration and calibration method.calibration and calibration method.

The GIS tools in framework is helpful of delineating catchments and dividing

them into many sub-catchments.

Out of the 11 models provided in the Source’s Modelling Platform, GR4J is

selected as they are the simplest and have wide application area.

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• Methodology


• Conclusion

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Study Objectives

• Calibration, validation and determination of parameters of the models.

• Comparison of rainfall runoff models GR4J and SWAT for a medium sized catchment in

Upper Godavari River Basin.Upper Godavari River Basin.

• Discussing the limitations and scope of the models.

1/19/2018 9






• Methodology


• Conclusion

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Stands for Soil and Water Assessment Tool.

It is physically based, semi distributed model.

It works on daily time steps.

SWAT

It divides the catchment into a number of Sub-basins and then into Hydrological

response units with respect to Land use , Slope and Soil data.

Being a distributed model, the data requirement of this model consists of various

meteorological and non-meteorological inputs.

Source: Neitsch et al. 2009

Stands for Ge´nie Rural a` 4 parame`tres Journalier.

It belongs to the family of soil moisture accounting models.

It is a continuous lumped conceptual model which operates in daily step.

The parameters used in this model are independent and represent the components

of conceptual hydrological model.

GR4J

of conceptual hydrological model.

Using Source platform a catchment can b e divided into many sub catchments

manually which are also called functional units and parameters can be calibrated

for each unit separately.

The data used for this model are rainfall (P) and potential evapotranspiration

(E).

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GR4J - Model Structure

ParametersDescription of

Parameters Units Range Significance

x1

Maximum capacity of the

production store mm 1-1500

Represents Soil store from which

losses occurWater

exchange Quantifies Baseflow

Source: Perrin et al,2003

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x2

exchange coefficient mm

Baseflow contribution

x3

Maximum capacity of the routing store mm 1-500

Represents Soil store which

contributes to stream flow

x4

Time Parameter for

unit hydrographs day 0.5-4

Controls peak flow occurrence






• Methodology


• Conclusion

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STUDY AREA

• Catchment Location: Ahmednagar

district, Maharashtra

• It is delineated with respect to the

Mula dam which is the 2nd largest

dam after Paithan dam in Upper

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Godavari Region.

• Catchment Area: 2300 km2

• Primary land-use pattern are barren

and agricultural lands.

• Soil type: Loam

• Water Resource Usage: Mainly for

Irrigation and Drinking

DATA TYPE SOURCE SCALE/PERIODS

DATA DESCRIPTION

DEMSRTM digital elevation data

produced by USGS30m x 30m Terrain properties.

SOILFAO (Food and Agricultural

Organization)1/5000000 Soil classification and physical properties

NRSC, ISRO Hyderabad 28m*28m /2004 Land use classification(19 classes)

Data Requirement for SWAT

LAND USENRSC, ISRO Hyderabad 28m*28m /2004 Land use classification(19 classes)

CLIMATEIndian Meteorological

Department (IMD)0.25 degree / 2000-

2011Minimum and Maximum temperature, wind speed,

relative humidity, solar radiation

DISCHARGEWALMI,Aurangabad 2000-2011 Daily discharge data at selected station

PRECIPITATION WALMI,Aurangabad 2000-2011 Daily Rainfall for 102 Rain-gauges in Upper Godavari River Basin

Data Requirement of GR4J

•Digital Elevation Model from USGS’s Shuttle Radar Topography Mission is

used for delineation of the catchment area.

•The Meteorological inputs required are : Precipitation Data and Potential

Evapotranspiration Data

• PET data is collected from 2 stations Inside the Mula catchment.

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• Representative precipitation

for the catchment is

calculated using Theissen

Polygon Method for

available rain gauges in

Upper Godavari River

Basin.

• PET data is collected from 2 stations Inside the Mula catchment.






• Methodology


• Conclusion

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Calibration and Validation

Models GR4J SWATCalibration Tool Source’s Calibration Wizard. SWAT -CUP

Calibration Method Shuffled Complex Evolution then Rosenbrock’s Function

SUFI2

Objective Function: NSE daily and bias penalty NSE

Number of SCE Complexes 7 --

Number of Parameters used for calibration

4 11

Number of Iterations 1000 1000 simulations

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Number of Iterations 1000 1000 simulationsIterated 4 Times

Performance Evaluation Parameters for Calibration

•NSE•R^2

•NSE•R^2•PBIAS•P-factor•R-factor

Calibration Period 2000-2007 2000-2007

Validation Period 2008-2011 2008-2011

Scale Monthly Monthly

Model Set Up and Simulation Calibration and Uncertainty Analysis

Parameters Initialization

Setting up SWAT-CUP for extraction of characteristics of

catchment to be calibrated

SWAT

Selection of Objective Function

Sensitivity and Uncertainty Analysis Results

Is the Criteria Satisfied?

Stop

New Parameters

Draw network using sub-catchment raster

Input Digital Elevation Model

Geographic Scenario

Selection of Outlet node

Selection of Rainfall-Runoff Model

Assigning Functional Units

Stream Computation

Use DEM

?Yes No

Model Setup

GR4J

22Validation

Rainfall data

Potential Evapotranspiration DataInput data

Optimized values of x1,x2,x3 and x4

Calibration Run

Selection of Optimization Algorithm

Selection of Objective Function

Grouping the parameters to be calibrated

Selection of Rainfall-Runoff Model

Calibration Setup






• Methodology


• Conclusion

19-01-2018 23

Calibration Validation

NSE 0.78 0.65

SWAT Model Results

0

5

10

15

20250

300

350

400

Rai

nfal

l (m

m)

Stre

amfl

ow (

cum

ecs)

95PPU Rainfall Observed Best Simulation

R^2 0.78 0.67

PBIAS -1.7 25.6

p-factor 0.77 0.59

r-factor 0.49 0.55

20

25

30

35

40

45

500

50

100

150

200

250

Jan/

00

Jul/

00

Jan/

01

Jul/

01

Jan/

02

Jul/

02

Jan/

03

Jul/

03

Jan/

04

Jul/

04

Jan/

05

Jul/

05

Jan/

06

Jul/

06

Jan/

07

Jul/

07

Jan/

08

Jul/

08

Jan/

09

Jul/

09

Jan/

10

Jul/

10

Jan/

11

Jul/

11

Rai

nfal

l (m

m)

Stre

amfl

ow (

cum

ecs)

Time

Parameter_Name Fitted_ValueV__CN2.mgt 34.54417

V__GW_REVAP.gw 0.073399V__ESCO.hru 0.871966V__CH_N2.rte 0.045024V__CH_N2.rte 0.045024V__CH_K2.rte 64.824745

V__ALPHA_BNK.rte 0.103641V__SOL_AWC(..).sol 0.93245

V__SOL_K(..).sol 1166.42627V__ALPHA_BF.gw 0.044351V__RCHRG_DP.gw 0.566686V__HRU_SLP.hru 0.177506V__REVAPMN.gw 189.188705

GR4J Model Results

x1 x2 x3 x4

127.76 2.46 59.69 2.82

Calibrated Parameters

0

1

2

3

4

5

6

710

15

20

25

30

35

mm

Cum

ecs

Calibration Validation NS 0.82 0.57

R^2 0.84 0.61

7

8

9

100

5

10

Jan/

00

Jun/

00

Nov

/00

Apr

/01

Sep

/01

Feb

/02

Jul/

02

Dec

/02

May

/03

Oct

/03

Mar

/04

Aug

/04

Jan/

05

Jun/

05

Nov

/05

Apr

/06

Sep

/06

Feb

/07

Jul/

07

Dec

/07

May

/08

Oct

/08

Mar

/09

Aug

/09

Jan/

10

Jun/

10

Nov

/10

Apr

/11

Sep

/11

Time

Rainfall Simulated Observed

Comparison

GR4J SWATType Conceptual model Semi-Distributed model

Parameters 4More than 20 parameters to calibrate using streamflow

Structure Simple structure Complex structure

Data RequirementRequires only two input variables, PET and Rainfall

Requires meteorological properties such as wind speed, relative humidity, Temperature, etc. and physical watershed properties such as LULC, soil along with rainfall as input.

Time requirement Low HighTime requirement Low High

SuitabilitySuitable for data scarce regions

Not suitable for data scarce regions

Calibration Deterministic calibration Stochastic calibration

Output StreamflowIt can generate other hydrologic processes along with stream flow

Catchment characteristics

Does not consider physical properties of the watershed for modeling

Considers physical properties of the watershed for modelinge.g., LULC, Soil

Scale of catchmentProvides best results in small catchments

Provides best results in small as well as large catchments






• Methodology


• Conclusion

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• GR4J has 4 independent parameters, SWAT has many interdependent physically based

parameters.

• The parameters of GR4J give a vague idea about catchment characteristics , where as that of

SWAT being physically based helps us understand various hydrological processes in the

catchment concerned.

• The calibration process for GR4J is faster than SWAT due to it lower complexity.

Conclusions

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• The calibration process for GR4J is faster than SWAT due to it lower complexity.

• The performance of both GR4J and SWAT are similar in terms of NSE and R^2.

• Conceptual models such as GR4J can be used as effective water resource modelling tools in

data scarce areas for short term analysis and prediction.

• Due to unreliability of parameters , GR4J can’t be used for long term prediction and analysis. In

such cases, distributed model such as SWAT can be used.

ACKNOWLEDGEMENT

We are thankful to Water And Land Management Institute (WALMI),Aurangabad and eWater, Australia for providing necessary data for this study.

CITED REFERENCES

•Boughton, W., (2004). The Australian water balance model. Environmental Modelling & Software,

19(10),

943–956.

•eWater(2013),Source User Guide (v3.5.0)[Online]

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•eWater(2013),Source User Guide (v3.5.0)[Online]

(https://eWater.atlassian.net/wiki/display/SD37/Source+User+Guide)

•Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water

assessment tool theoretical documentation version 2009. Texas Water Resources Institute.

•Perrin, C., Michel, C., & Andréassian, V., (2003). Improvement of a parsimonious model for stream flow

simulation. Journal of Hydrology, 279(1–4), 275–289.

•USGS (2004), Shuttle Radar Topography Mission, 1 Arc Second scene SRTM_u03_n008e004, Unfilled

Unfinished 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland, February 2000.

•WALMI (2017) report, Upper Godavari Catchment Calibration Model

Any Questions /Suggestions?Any Questions /Suggestions?

19-01-2018 31

Thank YouThank You

19-01-2018 32

Thank YouThank You

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