hydrological evaluation with swat model and numerical

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Kasetsart University

Winai WANGPIMOOL1

2012 International SWAT Conference & Workshops

Hydrological Evaluation with SWAT Model and Numerical Weather Prediction for Flash Flood Forecast System: a Case Study for Upper Nan Basin in Thailand.

New Delhi, India 20 July 2012

1 Ph.D. Student, Kasetsart University 2 Associate Professor, Ph.D., Kasetsart University 3 Associate Professor, Ph.D., Chiang Mai University 4 Thai Meteorological Department 5 Mekong River Commission

Kobkiat PONGPUT2 , Thanaporn SUPRIYASILP3

Kamol P.N. SAKOLNAKHON4 , Ornanong VONNARART5

Presented by:

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1. Introduction

2. Methodology 3. Model configuration &Results 4. Discussions & Recommendations 5. Conclusions

Contents

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Study area

Nan province

General Information

Catchment area 5,663 km2

River length 240 km

Average Temperature 25.6 degree Celsius

Annual rainfall 1,382 mm

Annual runoff 1,638 MCM

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Kasetsart University 1. ∆”¢À¥“¡€¬÷ºƒ“‡∏…ª ƒ÷‡»¥∂ç∫ ∫ ÌÈ‘ Topography: Long Profile

แม่น�ําน่านตอนบนที�มีความลาดชันสูง

Tung Chang Chiang Klang Muang Wiang Sa

1:480

1:3,500 1:5,300

1:3,000

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Problems

Nan City Mountainous area terrain with steep slope Rainfall and runoff stations are less because the access to the station area is difficult and high cost for maintenance

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Kasetsart University 2. ∆”¢À¥“øŸÈ∫∏◊Ëºå‘∂ç∫ ∫ ÌÈ‘

Chiang Klang, Pua and Bo Klure district area

Upstream

Problems

Sources: Department of Water Resources, 2011

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Kasetsart University Flooded on June 2011

Nan City

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Kasetsart University Flooded on June 2011

Nan City

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Kasetsart University Flash Flood on June 2006

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Objectives and Scope

1. Used Soil and Water Assessments Tool (SWAT) model for the hydrologic study to evaluate runoff occurring in the watershed system

2. Numerical Weather Prediction (NWP) weather data forecast for the next 3 days was used for stream flow estimation by SWAT model.

3. Stream flow estimation at outlets and can then be used for hydrodynamic models to create a flood map for flash flood warning system.

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Overall Methodology

•GIS Data

•Time Series Data

•Model Simulation Data

•Miscellaneous Data

Hydrological Model (SWAT)

Numerical Weather Prediction Model

(NWP)

Hydrodynamic Model

(HEC-RAS)

Extension (HEC-GeoRAS)

Flood risk map (ArcGIS)

Displays

(Google Earth)

Decision Maker

For Technical user

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Methodology SWAT Model

Results:

Stream flow were estimated at outlets in next 3 days

Forecasting Data by NWP in next 3 days

Weather (Precipitation, Temperature, Solar radiation, Humidity, Wind speed)

Validated NWP–SWAT

(Reanalysis NWP data Year 2006)

NWP Model

Calibrated /Validated

Y

N

Y

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1-Spatial Data 1.1-Administrative Data from DWR

Administrative boundaries River layouts Catchment's boundaries Drainage network Hydro-meteorological station

1.2-Physical Data from LDD Digital elevation model: 30m x30m Land use /Land cover map Soil classification map

Data Used

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2-Time Series Data 2.1-Weather Data

Maximum / Minimum Temperature Solar radiation Wind speed Relative humidity Evaporation

2.2-Hydrological Data

River flow 4 stations from RID and 2 sta. from DWR

Rainfall 11 stations ,From TMD

Only 1 station ,From TMD for

Data Used

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Global scale/synoptic scale

Regional scale Local scale

Downscaling technique for NWP

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Kasetsart University NWP Data from TMD.

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Kasetsart University DOMAIN2 2 km.(crop) - latitude 17.95 – 19.7 - Longitude 100.3 – 101.4

NWP provided: Rainfall, Temperature, Humidity, Solar radiation and Wind speed data.

NWP Data

Done by Hydrologist team from Thai Meteorological Department (TMD)

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Delineated Watershed into 28 Sub basins

SWAT Model Set up

Simulation Periods: 1. Calibration: 1993 – 2008(16 years) 2. Validation: 2009 (1 year)

6 Calibration Points

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Sensitivity Analysis

1. Surlag 2. SOL_AWC 3. CN2 4. ESCO 5. SOL_Z 6. CH_K2 7. SOL_K 8. CANX 9. ALPHA_BF 10. CH_N

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Kasetsart University Calibration Results

No. Station Name of Station CA. Calibration Sub-basin R2

Volume Ratio Peak Discharge - cms

Code (sq.km.) Period (%) Observe Simulate Sim / Obs (%)

Main river

1. N.1 Muang, Nan. 4,609.0 1994-2007 S_17 0.89 108.95 2,636 2,327 88.29

3. N.64 Ban Pa Khuang 3,440.1 1994-2004 S_12 0.90 110.27 2,281 1,889 82.81

Tributaries

1. N.49 Nam Yao @ Pua 155.0 1994-2004 S_13 0.22 100.47 352 295 83.78

2. N.65 Nam Yao @ Pang Sa 611.8 1997-2002, 2004-2007 S_11 0.87 113.21 442 385 87.08

3. 090201 Nam Pua @ Ban Na Phang 146.7 1994 - 2007 S_7 0.45 99.47 461 284 61.50

4. 090203 Nam Korn @ Ban Pa Dang 176.0 1994-2006 S_5 0.51 99.83 124 125 100.89

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Kasetsart University Nam Korn at d090203 station (Sub-05)

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Kasetsart University Nam Pua at Staion d090201 , Sub-07

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Kasetsart University Nam Yao at N65 station (Sub 10-11)

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Nan river at N64 station , Sub-12

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Nan river at N1 station , Sub-17

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Kasetsart University NWP Application

Using NWP data generated from 2000-2010 to replace on historical data

Before used NWP data in the next 3 days: Checked and Improved by bias correlation technique

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Kasetsart University NWP Results

-

500.0

1,000.0

1,500.0

2,000.0

2,500.0

3,000.0

5/1/

2006

5/15

/200

6

5/29

/200

6

6/12

/200

6

6/26

/200

6

7/10

/200

6

7/24

/200

6

8/7/

2006

8/21

/200

6

9/4/

2006

9/18

/200

6

10/2

/200

6

10/1

6/20

06

10/3

0/20

06

Obs N64

NWP_Sim. N64

-

500.0

1,000.0

1,500.0

2,000.0

2,500.0

3,000.0

3,500.0

5/1/

2006

5/15

/200

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5/29

/200

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6/12

/200

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6/26

/200

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7/10

/200

6

7/24

/200

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8/7/

2006

8/21

/200

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9/4/

2006

9/18

/200

6

10/2

/200

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10/1

6/20

06

10/3

0/20

06

Obs N1

NWP_Sim.N1

At N64 station

At N1 station

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Kasetsart University NWP Results

Main River: higher 10-20% Tributary: lower 10-20%

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The result of runoff study with SWAT model in the Upper basin showed that Average annual runoff = 1,638.5 MCM

The average runoff occurred during the rainy season (June-November) was about 1,421 million cubic meters (87% of the average annual runoff) and in the dry season about 217.5 million cubic meters (13 % of the average annual runoff)

SWAT Model Results

Use for inflow boundary of flood study

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Kasetsart University Example results : Flood Risk Map

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Display with GE

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Kasetsart University Discussions

0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

Observed

NWP

Historical

NWP provided higher peak than the others. However, considering the shape of hydrograph after the peak, the simulated discharges from NWP decreased faster than the others

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Kasetsart University Recommendations

1. NWP provided weather data in grid format same as DEM, Land use and Soil class. It’s better if we can input weather data directly into SWAT model.

2. For flood season, Necessary to monitor and used hourly data. It should be simulated by use hourly data.

3. In future work, We will develop and improve the system to automatic simulation and display results.

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● SWAT can be done to generated stream flow as well.

● NWP climate data were compatible with the measured data except rainfall data needed to be adjusted by bias correlation before being applied in the SWAT model.

● All data from the NWP can be used with the SWAT model and they were provided a good results.

● NWP data is very useful for hydrologic study in case of lack the weather data from observed station.

● The SWAT output can then be used for Hydrodynamics model to create a flood map for flash flood warnings system.

Conclusions

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Acknowledgements

The authors thank the Thailand Research Fund (TRF) through the Royal Golden Jubilee Ph.D. Program for their financial support.

Thanks to the Department of Water Resources Thai Meteorological Department and other line agencies for supplying the data for the study.

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Thank you for your kind attention…

hydrological evaluation with swat model and numerical

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