Management Scenario for the Critical Subwatersheds of Small Agricultural Watershed using SWAT model and GIS Technique

Transcription

1 Management Scenario for the Critical Subwatersheds of Small Agricultural Watershed using SWAT model and GIS Technique Authors: M.P. Tripathi, N. Agrawal and M.K. Verma Indira Gandhi Krishi Vishwavidyalaya Raipur (C.G.) India and National Institute of Technology Raipur (C.G.) India

2 Objectives To calibrate and validate the physically based hydrological and water quality simulation model for the small watershed. To estimate surface runoff, sediment yield and nutrient losses from a small watershed using SWAT model, satellite data and GIS. To identify the critical sub-watersheds on the basis of estimated sediment yield and nutrient losses for multiple years. To develop and recommend the best management practices for the critical sub-watersheds of the study watershed.

3 CHHATTISGARH IN INDIA N Chhokranala Watershed Outlet Longitude: to E Latitude: to N Geographical area-1731 ha Location map of the Chhokranala Watershed

4 Salient Features of Chhokranala Watershed Altitude: 290 m to 310 m above MSL Area of watershed: 1731 Ha No of villages covered: 6 Weighted average slope : 1.6 % Predominant soil: Sandy clay loam Avg. annual rainfall: 1400 mm Mean monthly temp: Max C Mean monthly temp: Min. 7 0 C Mean RH: Min. 38 % Max. 83 % Over all climate: Sub-humid tropic

5 RSOP at the outlet of Chhokranala watershed

6 DEM SWAT Arc GIS Interface Output ERDAS Arc GIS Time series Observed runoff Simulated runoff Precipitation Data base Runoff (mm) Jun-98 Jul-98 Aug-98 Sep-98 Oct-98 Month Precipitation (mm) SWAT GIS Interface

7 Contour map of the Chhokranala watershed

8 Digital Elevation Model (DEM) of the watershed

9 Sub watershed map of the watershed

10 LAND USE/LAND COVER CLASSIFICATION The path 21, row 55, scenes of IRS-1C (LISS-III) satellite with date of pass 5th October 2002 is used for the study to prepare the land use/land cover maps of both the watersheds. Supervised classification, has been adopted for classifying the land use of the study watershed. This is an interactive approach in which the operator classifies an area or group of pixels that belong to one or more categories of specific land use/land cover. Variouslandusesaregivendifferentcoloursforeasy identification.

11 Land use/cover map of the watershed

12 Area under different land use in the Chhokranala WS Land use class No. of pixels Area (ha) % Image Water body Grasses & shrubs Orchard Crop land Settlement Barren land Fallow land Total

13 Soil texture map of the watershed

14 Soil texture prevailing in the Chhokranala WS Soil type Local name Area (ha) % area Alfisols (Loam) Dorsa Inceptisols (Sandy clay loam) Matasi Vertisols (Clay) Kanhar Entisols (Sandy loam) Bhata

15 Physical and chemical properties of soil Particulars Bhata Matasi Dorsa Khanar Soil depth (cm) >100 Sand (%) Silt (%) Clay (%) Bulk density (g/cc) Infiltration (cm/hr) Available water (cm) ph

16 Subwatershed Sub-watershed wise values of various input parameters for the SWAT model Area (ha) Slope (%) Curve Numbers Av. slope length (m) Channel length (km) Channel slope (%) K value P value WS WS WS WS WS WS WS WS

17 Calibrated parameters of the model for the watershed S. No. Calibrated parameters Values chosen Prescribed range 1 Base flow factor Effective HC Channel 'n' value Overland flow 'n' value Fraction of field capacity

18 Rainfall Observed Simulated Runoff (mm) /1 7/1 8/1 9/1 10/1 Time (days) Rainfall (mm) Graphical comparison of observed and simulated daily runoff (Calibration years 2003)

19 Regression line 1:1 line Simulated runoff (mm) y = x r 2 = Observed runoff (mm) Scattergram between observed and simulated daily runoff (Calibration years 2003)

20 Results of statistical analysis for model calibration Daily (2003) Statistics Runoff (mm) Sediment yield (t/ha) Observed Simulated Observed Simulated Mean Standard deviation Maximum peak Total Count t-calculated t-critical (two tailed) r % deviation COE

21 Rainfall Observed Simulated Sediment yield (t/ha) Rainfall (mm) Graphical comparison of observed and simulated daily sediment yields (Calibration years 2003) /1 7/1 8/1 9/1 10/1 Time (days) Regression line 1:1 line 3 y = x Scattergram between observed and simulated daily sediment yield (Calibration years 2003) - Simulated sediment (t/ha) r 2 = Observed sediment (t/ha)

22 Rainfall Observed Simulated Runoff (mm) Rainfall (mm) Months 900 Graphical comparison of observed and simulated monthly runoff (Calibration years )

23 Results of statistical analysis for model calibration Monthly ( ) Statistics Runoff (mm) Sediment yield (t/ha) Observed Simulated Observed Simulated Mean Standard deviation Maximum peak Total Count t-calculated t-critical (two tailed) r % deviation

24 Regression line 1:1 line y = 1.017x r 2 = Simulated runoff (mm) Observed runoff (mm) Scattergram between observed and simulated monthly runoff (Calibration years )

25 12 Rainfall Observed Simulated 0 Sediment yield (t/ha) Rainfall (mm) Graphical comparison of observed and simulated monthly sediment yields (Calibration years ) Months Scattergram between observed and simulated monthly sediment yield (Calibration years ) Simulated sediment yield (t/ha) Regression line y = 1.219x r 2 = :1line Observed sediment yield (t/ha)

26 Results of statistical analysis for model validation Daily (2004) Statistics Runoff (mm) Sediment yield (t/ha) Observed Simulated Observed Simulated Mean Standard deviation Maximum peak Total Count t-calculated t-critical (two tailed) r % deviation COE

27 Results of statistical analysis for model validation Monthly ( ) Statistics Runoff (mm) Sediment yield (t/ha) Observed Simulated Observed Simulated Mean Standard deviation Maximum peak Total Count t-calculated t-critical (two tailed) r % deviation

28 Results of sensitivity analysis of calibrated SWAT model Parameters Values Annual runoff % deviation Annual sediment % deviation (mm) from base (t/ha) from base Overland 'n' Channel 'n' FFC* Base variable Calibrated

29 Statistical analysis of the observed and simulated nutrient losses ( ) Statistics Organic N Organic P NO 3 -N Soluble P Obs. Sim. Obs. Sim. Obs. Sim. Obs. Sim. Mean Standard deviation Maximum Total Count t-calculated t-critical (two tailed) r % deviation

30 Results of statistical analysis for model validation for daily rainfall generation Statistical parameters Mean (mm) Standard deviation Maximum (mm) Total (mm) Count t-calculated t-critical (two-tail) 1.96 r % deviation Chhokranala watershed ( ) Observed Simulated

31 Results of statistical analysis for model validation for monthly rainfall generation ( ) Statistical parameters Mean (mm) Standard deviation Maximum (mm) Total (mm) Count t-calculated t-critical (two-tail) r % deviation Chhokranala watershed Observed Simulated

32 Statistical analysis for the monthly observed and simulated rainfall, runoff and sediment yield during the monsoon period Statistical parameters Rainfall (mm) Runoff (mm) Sediment (t/ha) Observed Simulated Observed Simulated Observed Simulated Chhokranala watershed Mean Standard deviation Maximum Total Count t-calculated t-critical (two-tail) r % deviation

33 Identification of critical sub-watersheds Sub- Watershed Area (km 2 ) Runoff (mm) Sediment (t/ha) Organic N (kg/ha) Organic P (kg/ha) NO 3 -N (kg/ha) Soluble P (kg/ha) Erosion class Priority WS M - WS M - WS H - WS H - WS V. H. I WS H II WS H III WS Soil erosion classes Slight Moderate High Very high Severe Very severe Soil erosion range >80

34 Tillage treatments Tillage treatments Code Mixing efficiency Zero tillage T Conservation tillage T Field cultivator T M. B. plough T Conventional tillage T Fertilizer levels for different crops Fertilizer level (code) Rice Maize G-nut Soybean Existing (F1) 25:15 (5:5) 1/2 of the recommended (F2) 40:30 (10:10) Recommended (F3) 80:60 (20:20) 20:15 (5:5) 50:30 (10:5) 100:60 (20:10) 10:20 (5:5) 20:40 (10:5) 30:60 (15:10) 10:20 (5:5) 30:30 (10:5) 60:60 (15:10)

35 WS5 of Chhokranala Watershed Crop Runoff (mm) Sediment (t/ha) Organic N (kg/ha) Organic P (kg/ha) NO 3 -N (kg/ha) Soluble P (kg/ha) Grain yield (t/ha) Rice G-nut Maize Soybean

36 Results of Management for WS5 (Chhokranala Watershed) Treatments Runoff (mm) Sediment (t/ha) NO 3 -N (kg/ha) Soluble P (kg/ha) Organic N (kg/ha) Organic P (kg/ha) T1+F T2+F T3+F T4+F T5+F T1+F T2+F T3+F T4+F T5+F T1+F T2+F T3+F T4+F T5+F

37 Conclusions 1. Manning's 'n' values for overland flow and channel flow are and 0.025, respectively for the Chhokranala watershed. 2. The SWAT model accurately simulates monthly runoff and sediment yield from the Chhokranala watershed. 3. The SWAT model accurately simulates nutrient losses from the Chhokranala watershed on daily basis. 4. The weather generator can be used to simulate monthly rainfall and thereby runoff and sediment yield. The model can be used for planning and management of the small agricultural watersheds on long-term basis using generated daily rainfall.

38 5. The SWAT model can successfully be used for identifying critical sub-watersheds for management purpose. 6. The sub-watershed WS5 and WS6 and WS7 of Chhokranala watershed found to be critical. 7. Crops like maize, groundnut and soybean can not replace the existing rice crop, on the basis of sediment and nutrient losses reduction criteria. 8. Zero tillage, conservation tillage and field cultivator along with 40:30 kg/ha of N:P can be recommended because these tillage practices reduce sediment yield as compared to existing tillage and nutrient losses being within the permissible limit.

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