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Evaluation of Daily TMPA Rainfall for Rainfall-Runoff Modelling Using SWAT in Indravati River Basin, India International Conference on Soil and Water Assessment Tool 2018 (SWAT-2018) Date: 12 th Jan 2018 By Praveenkumar. C 1 and V. Jothiprakash 2 1 Research Scholar, 2 Professor, Department of Civil Engineering, IIT Bombay. Corresponding author email: [email protected]

Transcript of 1. Praveen Kumar Chelluri - Texas A&M University · ,QWURGXFWLRQ ¾t ] } v } ( Z u } ] u } v v µ o...

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Evaluation of Daily TMPA Rainfall for Rainfall-Runoff Modelling Using SWAT in Indravati River Basin, India

International Conference on Soil and Water Assessment Tool 2018

(SWAT-2018)

Date: 12th Jan 2018

By

Praveenkumar. C1 and V. Jothiprakash2

1Research Scholar, 2Professor,

Department of Civil Engineering, IIT Bombay.

Corresponding author email: [email protected]

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Contents

• Introduction

• Aim and Objectives

• Methodology

Praveenkumar. C and V. Jothiprakash, IIT Bombay

• Study area and datasets used

• Results and discussions

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Introduction

Water is one of the most important natural resources which support the life of living organisms

Due to an increase in population, there is a more demand for water from various sectors

Hydrological modeling is the standard approach to estimate surface runoff

Rainfall is the major driven force in hydrological modelling

Recently remote sensing rainfall data application is increasing in water related studies

Praveenkumar. C and V. Jothiprakash, IIT Bombay

Tropical Rainfall Measuring Mission (TRMM) rainfall data is having a great interest among the research

community due to the origin of data from multi satellites

merging of TRMM rainfall with in-situ rain gauge measurements has extended their application

Huffman and Bolvin, (2013) developed an advanced version of TRMM 3B42 version7 namely

TRMM MultiSatellite Precipitation Analysis (TMPA)

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Introduction contd.,

Prakash et al., (2015) carried out validation of TMPA rainfall data with observed gauge rainfall data at

different time scales over the selected regions in India

Along with rainfall data, selection of rainfall-runoff model also very important

SWAT is a widely used semi distributed hydrological model which is an open source model

Praveenkumar. C and V. Jothiprakash, IIT Bombay

Being SWAT as a semi distributed model, the constraint in data availability can also be reduced (Arnold

et al., 1998; Arnold et al., 2012)

SWAT model is applied at different geographical locations of the earth for quantifying daily surface

runoff

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Objective

To evaluate the daily TMPA rainfall for rainfall-runoff modelling using SWAT

model in Indravati river basin, a sub-basin of Godavari river, India

Methodology

Praveenkumar. C and V. Jothiprakash, IIT Bombay 5

Figure 1. Flow chart of the methodology

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Study Area

The study area Indrāvati river basin is a sub basin of the Godavari river, India

The Indrāvati basin covers a geographical area of 38,963.78 km2

Praveenkumar. C and V. Jothiprakash, IIT Bombay 6

Figure 2. Location map of Indravati river basin with TMPA grid stations

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Datasets used

Discharge

- The discharge data available from 2001 to 2013 at catchment outlet located in

Pathagudem, Maharashtra

(a) (b)

Praveenkumar. C and V. Jothiprakash, IIT Bombay 7

Figure 3. (a) Time series plot of the observed discharge, (b) Stage discharge relationship at Pathagudem (out let of Indravati river basin)

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Data used Contd.,

Land Use / Land Cover (LU/LC) data

- Indravati river basin is having forest area as a dominant LU/LC and irrigated or

agriculture land as a second dominant LU/LC

Praveenkumar. C and V. Jothiprakash, IIT Bombay 8

Figure 4. Land Use / Land Cover Map of Indrāvati basin

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Data used Contd.,

Soil data

Indravati river basin includes clay loam soil as a dominant soil

There exist eight different types of soil classes within the river catchment area

Those are sandy clay loam, clay loam, clay, sandy loam, silt, and silt loam are the major

soil groups in this basin

Praveenkumar. C and V. Jothiprakash, IIT Bombay 9

Figure 5. Soil map of Indrāvati basin(source: webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database)

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Data used Contd.,

Digital Elevation Model (DEM)

The Indravati rises on the western slopes of Eastern Ghats

The highest elevation is 1325 m

Praveenkumar. C and V. Jothiprakash, IIT Bombay 10

Figure 6. Digital elevation map of Indrāvati basin (Source: bhuvan.nrsc.gov.in)

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Data used Contd.,

Rainfall

The daily TMPA rainfall with 79 grid station pertaining to Indravati river basin is used.

The study period of 13 years from 2001 to 2013 is considered

Reservoir Outflow

The reservoir outflow data provided by CWC for upper Indravati reservoir is collected

Praveenkumar. C and V. Jothiprakash, IIT Bombay

This data is available from 1st Jan 2005 to 31st May 2017. The data period from 2005 to

2013

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Figure 7. Daily reservoir outflow data at upper Indravati reservoir

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Sensitivity analysis of SWAT model parameters

S. No. Parameter name Parameter descriptionInitial

value

Parameter range Best fitted value

Lower bound Upper bound TMPA rainfall

1. a_CN2.mgt SCS runoff curve number 70 0 0.2 13.01

2. v_ALPHA_BF.gw Base flow alpha factor (days) 0.048 0 0.1 0.075

3. v_GW_DELAY.gw Ground water delay (days) 31 20 60 57.692

4. a_GWQMN.gw Threshold depth for ground water

flow to occur (mm)

1000 100 1000 921.07

5. a_CH_K2.rte Effective channel hydraulic

conductivity (mm/hr)

0 0 100 84.37

Table 1. Best fitted values of SWAT model parameters for TMPA rainfall

Praveenkumar. C and V. Jothiprakash, IIT Bombay 12

conductivity (mm/hr)

6. v_OV_N.hru Manning’s N 0.01 0.1 7 5.934

7. v_SURLAG.bsn Surface runoff lag coefficient (days) 4 0.1 20 17.24

8. v_ESCO.bsn Soil evaporation compensation factor 1 0.6 1 0.668

9. v_EPCO.bsn Plant uptake compensation factor 1 0.6 1 0.827

10. v_RCHRG_DP.gw Deep aquifer percolation fraction 0.05 0.01 0.2 0.129

11. v_GW_REVAP.gw Ground water “revap” coefficient 0.02 0.01 0.2 0.135

12. r_SOL_AWC (...).sol Available water capacity of the soil

layer (mm H2O/mm sol)

1 0.1 1 0.46

13. v_SOL_BD (…).sol Moist bulk density (g/cm3) 2.5 0.1 2 0.84

14. r_SOL_K (...) .sol Saturated hydraulic conductivity

(mm/hr)

2000 0 0.2 0.133

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Daily runoff estimation of SWAT model

The runoff estimations of SWAT model with TMPA daily rainfall

- During the calibration period, R2 and NSE values are 0.35 and 0.3 respectively

- During the validation period, these values are 0.51 and 0.45 respectively

(a) (b)

Praveenkumar. C and V. Jothiprakash, IIT Bombay 13

Figure 8. Daily runoff estimated during calibration period using SWAT model with considering reservoir

(b)(a)

Figure 9. Daily runoff estimated during validation period using SWAT model with considering reservoir

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Monthly runoff estimation of SWAT model

The runoff estimation of SWAT model forced with TMPA monthly rainfall

- During the calibration period, R2 and NSE values are 0.35 and 0.3 respectively

- During the validation period, these values are 0.51 and 0.45 respectively

(a) (b)

Praveenkumar. C and V. Jothiprakash, IIT Bombay 14

(b)(a)

Figure 10. Monthly runoff estimated during calibration period using SWAT model with considering reservoir

Figure 11. Monthly runoff estimated during validation period using SWAT model with considering reservoir

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Results of SWAT model simulations

The results indicates that, SWAT model

estimated runoff more accurately during

validation period than calibration period

This may be due to the under estimation

of TMPA maximum rainfall at higher

elevations

The addition of reservoir data improved

the accuracy of model estimations in

S.No. SWAT simulation

Time Step

Daily Monthly

R2 NSE R2 NSE

Calibration period 2002 to 2008

1. Without Reservoir 0.29 0.26 0.72 0.65

2. With Reservoir 0.35 0.3 0.78 0.75

Validation period 2009 to 2013

3. Without Reservoir 0.51 0.45 0.86 0.76

4. With Reservoir 0.53 0.42 0.85 0.71

Table 2. Results of SWAT modelling in Indravati river basin

Praveenkumar. C and V. Jothiprakash, IIT Bombay

the accuracy of model estimations in

Indravati river basin

Since, the drainage area under this

reservoir is very less compared to basin

area, the reservoir effects need to be

studied at other basins with

consideration of more reservoirs

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Figure 12. Mean annual rainfall in Indravati river basin. a) IMD gridded and b) TMPA rainfall

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Conclusions

Around 1000 mm difference in TMPA maximum rainfall magnitudes is observed at a higher elevation

when compared to observed data

From the sensitive analysis of SWAT model parameters, ALPHA_BF, GW_REVAP, RCHRG_DP, SOL_K,

SOL_BD, ESCO and GW_DELAY are observed to be most sensitive parameters

Around 5 percent catchment area under upper Indravati reservoir improved the 10 percent of model

Praveenkumar. C and V. Jothiprakash, IIT Bombay

Around 5 percent catchment area under upper Indravati reservoir improved the 10 percent of model

accuracy for TMPA grid based SWAT model simulations at daily time step

Overall, TMPA rainfall, an open source data with global coverage is a promising dataset for estimating

surface runoff at large river catchments, especially at data scarce regions

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Important references

Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., ... & Kannan, N. (2012).

SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508.

Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment

part I: Model development1.

Huffman, G. J., and Bolvin, D. T. (2013). TRMM and other data precipitation data set documentation. NASA,

Praveenkumar. C and V. Jothiprakash, IIT Bombay 17

Greenbelt, USA, 1-40. https://pmm.nasa.gov/sites/default/files/document_files/3B42_3B43_doc_V7.pdf (Last access,

accessed 28 November 2016).

Prakash, S., Mitra, A. K., AghaKouchak, A., and Pai, D. S. (2015). Error characterization of TRMM Multisatellite

Precipitation Analysis (TMPA-3B42) products over India for different seasons. J. Hydrol. 529, 1302-1312.

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ThankYou

Praveenkumar. C and V. Jothiprakash, IIT Bombay

ThankYou

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