Application of SWAT: Assessing environmental efficiency of various land use scenarios in Haean catchment South Korea

Transcription

1 214 Session J1: Environmental Application SWAT 214 International SWAT Conference, Pernambuco, Brazil Application of SWAT: Assessing environmental efficiency of various land use scenarios in Haean catchment South Korea 1 st August 214 Ganga Ram Maharjan 1 / Christopher L. Shope 2 / Trung Thanh Nguyen 3 /Sebastian Arnhold 1 /Bernd Huwe 1 /Seong Joon Kim 4 /John Tenhunen 5 1 Department of Soil Physics, University of Bayreuth, Bayreuth, Germany 2 US Geological Survey, 2329 Orton Circle, Salt Lake City, UT, USA 3 Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 4 Dept. of Civil & Environmental System Eng. Konkuk University, Korea 5 Department of Plant Ecology, University of Bayreuth, Bayreuth, Germany International Research Training Grop, DFG/KOSEF

2 General issues of the Haean Catchment Objectives Model Setup (In put Maps) Results Conclusion

3 General issues of the Haean Catchment High Economic Activity Based on agriculture Yield oriented land use system increasing Urbanization and Deforestation Intensive dry and wet land Agriculture system Excess use of Fertilization Expert large amount of nutrients and Sediment Location of Study Area Source: Arnhold Haean Catchment Geographically located at 128 5' to 28 11'E, 38 13' to 38 2'N In Gangwon Provience near Demilitarized Zone (DMZ) between south and noth korea. Watershed Area: 62 Km 2 Annual Dry land Forest and Ochard Wet land/paddy 26.5% 62.5% 8.5% Elevation range m Annual precipitaion of 165 mm monstly concentrated (7%) within June - August. Consider as hot spot for muddy water discharge to Soyang reservoir. 3/19

4 2. Objectives To evaluate different land use system to determine environmentally efficient Land Use in retaining sediment and nutrients to the stream network. To evaluate economic efficiency - based on cost benefit analysis of efficient land use system environmentally To recommend best land use system by trade off analysis with other land use system. Experimental Setup: Field Campaigns Time Step Measurements Meteorological Data LAI and Biomass/Yeild Field level Erosion Stream discharge and sediment 4/19

5 4. Model Setup (In put Maps) Study area: Haean catchment, South-korea Watershed area: 62.7 km 2 Total number of sub-watershed: 21 Number of HRU formation: 792 MULTIPLE HRUs LandUse/Soil/Slope OPTION :THRESHOLDS : / / [%] Number of calibration points: 5 (S1, S2, S3, S4W, S5 and S7) Soil type Map Land Use Map DEM 5/19

6 Model Calibration and Validation A. Stream Flow Calibration and Validation 6/19

7 Model Calibration and Validation B. Sediment Calibration and Validation 7/19

8 Bio Mass and Yield Simulation 8/19

9 Development Of Land Use Scenario (Expansion Scenario) Four major Dry land crops Cabbage Soybean USLE_C=.2 Radish Potato Moderate Steep Dry farmland Crop Choice based on Market price Labour Farmers attitude toward Subsidies/Intervention others Base line Land use land cover map was analyze for 4 Major Crops Crops grown dominated in Moderate Steep Dry farmland and Flat Dry farmland land with soil texture of Sand Silt Flat Dry farmland Soil Type Texture Hydrologic group Area (km 2 ) Percentage catchment Flat dryland soil Sand Silt D Forest soil Loam Sand C Moderate steep dry land Sand Silt D Rice paddy soil Sand C Sealed ground caly D Very steep forest soil Loam Sand C /19

10 Application of Crop Expansion Scenarios Cabbage Expansion LULC Map 21 Radish Expansion RADI Soybean Expansion Potato Expansion RADI 1/19

11 Output Indicator for Different Scenario Surface runoff Biomass/Yield Sediment Crop Yield Linking Farm Income 11/19

12 Presumption/Constrains Farm Income Calculation Based on Crop Price Field Survey in 21 Field Survey (3 farms) N Income = (MYld P-PC)A i /A i=1 Where, Icome = Won/ha Myld = Marketable Yield ton/ha P = Price Won/kg PC = Production cost Won/ha N = Total Number of HRU A i = Area of Hru A = Total Area Production Cost: f (fertilizer, tillage, seed, labor) Crop Price (Price taker) Regional Price Crop allocation difficult Assumption of Extreme Scenario Biomass/yield: f(micro climate/water/nutrients) SWAT Scenario Farm Income: Yield Revenue Prod Cost Agri Based Economy Surface Runoff / Sediment Agri Based Environmental Effect 12/19

13 % Change from Base Line Scenario Results for Crop Expansion Scenarios Surface runoff (mm) Farm income (Million Won/ha) Sediment Crop Yield Expansion scenarios (ton/ha) (ton/ha) Base line Cabage Expansion Potato Expansion Radish Expansion Soybean Expansion Surface runoff Sediment Crop Yield Farm income Weighted mean Discharge (mm) Base line Expansion Scenario Weighted Mean Sediment (ton/ha) Base line Expansion Scenario Base line Cabage Expansion Potato Radish Expansion Expansion Weighted Mean Income (million won/ha) Base Income 43 Expansion Income Soybean Expansion CABG POTA SGBT SOYB Weighted Mean Yield (ton/ha) Base line Expansion Scenario 13/19

14 Land Use Optimization Sheet and Flow Chart S1 S2 S3 S4 Soil A.MStpDryS B. FlatDryS Soil A.MStpDryS B. FlatDryS Soil A.MStpDryS B. FlatDryS Soil A.MStpDryS B. FlatDryS Cabbage Raddish Soybean Potato Data Base of 4 SWAT Project Land Use System Optimize for Minimum Environmental Impact Farm Income Land Use System Optimize for Trade off HRU no LULC1 S1 Project S2 Project S3 Project Baseline LUS Optimization Env Surf/SE/YL D Income Rev-Prn LULC2 Env Surf/SE/YL D Income Rev-Prn LULC3 Env Income.. Mini (ENV) Max Income Max YLD Surf/SE/YL D Rev-Prn.. LULC LULC LULC 1 FRSD.... FRSD.... FRSD.... FRSD LU from S1 LU from S2 LU from BL 2 URBN.... URBN.... URBN.... URBN LU from S2 LU from S4 LU from S1 3 CABG.... RADI.... SOYB.... SOYB LU from BL LU from S1 LU from S4 4 RICE.... RICE.... RICE.... RICE LU from S3 LU from BL LU from BL.. CABG.... RADI.... SOYB.... SUNF LU from BL LU from BL LU from S LU from S4 LU from BL LU from BL CABG.... RADI.... SOYB.... POTA LU from BL LU from S1 LU from S1??Farm Income status: Simulate swat??env status: Simulate swat??farm Income N Env status : 14/19 Simulate swat

15 Optimize Land Use System 15/19

16 Results for Optimize Land Use System Base line Surface Runoff(mm) Minimize SR Minimize sed yield income Surface Runoff(mm) Base line Minimize SR Minimize sed yield income Base line Sediment (ton/ha) 12.6 Minimize SR 1.19 Minimize sed Base line Crop Yield (ton/ha) Minimize SR Minimize sed yield 4.8 yield income 1.6 income Sediment (ton/ha) Base line Minimize SRMinimize sed yield Crop Yield (ton/ha) Base line Minimize SRMinimize sed yield income 1.6 income Base line Profit (Mil.won/ha) Minimize SR Minimize sed 43.1 yield 67.4 income Profit (Mil.won/ha) Base line Minimize SR Minimize sed 43.1 yield 67.4 income 16/19

17 Trade off web plot Optimized land use scenarios Surface runoff (mm) Sedimen t (ton/ha) Crop yield (ton/ha) GMP (Million won/ha) Base line scenario Minimum SR scenario Minimum sediment scenario Maximum crop yield scenario Maximum income Scenario Optimized land use scenarios Surface Sedimen Crop runoff (%) t (%) yield (%) GMP (%) Base line scenario Minimum SR scenario Minimum sediment scenario Maximum crop yield scenario Maximum income Scenario Surface runoff (%) Sediment (%) Crop yield (%) GMP (%) Base line scenario Minimum SR scenario Minimum -4.8 sediment scenario Maximum crop yield scenario Maximum income Scenario 17/19

18 Conclusion Land use systems that produce Reduced surface runoff, Reduced sediment and yield were identified. Land use system which produce maximum yield and maximum income also produce higher Sediment and Surface Runoff. Land use system which produce minimize for surface runoff and sediment produce lower economic yield and lower income. Further reduction of sediment and surface runoff are possible from the recommended land use by applying effective filter width in the field of particular sub-basin. Optimized land use scenarios Surface runoff (mm) Sediment (ton/ha) Crop yield (ton/ha) GMP (Million won/ha) Base line scenario 1.6% 5% -67% -25% Minimum surface runoff scenario % -63% -11% Minimum sediment scenario.6% % -16% Maximum crop yield scenario 1.4% 47% % Maximum income Scenario 3.3% 46% -67%

19 Thank You,, Phd student, Ganga Ram Maharjan Department of Soil Physics, University of Bayreuth