SWAT modelling approach to assess the impacts of Best Management Practices (BMPs) in a potato crop by Natalia Uribe

Transcription

1 International SWAT conference June 28-30, Warsaw (Poland) 2017 SWAT modelling approach to assess the impacts of Best Management Practices (BMPs) in a potato crop by Natalia Uribe Dr. Marcela Quintero; Dr. Ann van Griensven; Dr. Gerald Corzo

2 Introduction The environmental authorities defined Best Management Practices (Conservational Tillage) as solution of Eutrophication process and degrades water quality caused by potato crop How BMP- Conservational Tillage in a potato crop could influence the soil and nutrients (N and P) losses in runoff at the watershed level?

3 Objectives General To assess the impacts of CT on sediments, nitrogen (N) and phosphorus (P) losses in runoff for potato crop at the watershed level Specific To simulate a CT extrapolation scenario for the entire potato crop area in Fuquene watershed

4 Study Area Fuquene Lake watershed Located North of Bogota, Colombia ( N, W) Area of approximately 784 Km 2 Elevation range between 2,520 asl and 3,780 asl Annual mean precipitation is 780 mm Mean annual temperature values are between 12 C and 18 C The agricultural activity is based on monocultures, mostly on potato crop

5 What has been done before? Identifying sources of pollution through isotopes Supporting the availability of machinery for tillage Adoption of Conservation Tillage (CT) practices as Best Management Practices

6 Conservational Tillage (CT) Intensive Tillage(IT) - Baseline Conservational tillage (CT) - Scenario Conventional Rotation: (PCCP) Intensive tillage (Rotovator bedder Invert the soil) Without vegetation cover Reduced tillage (chisel plow - vertical) Rotations with green manures (oat) Permanent soil cover (oat residue)

7 Input Data Intensive Tillage(IT) - Baseline Tillage (Rotovator-Bedder) Tillage (Rotovator-Bedder) Tillage (Rotovator-Bedder) Tillage (Rotovator-Bedder) Fertilazer Tillage (bedder shaper) Fertilazer Tillage (bedder shaper) Grazin 2 and 3 year Fertilazer Tillage (bedder shaper) Fertilazer Tillage (bedder shaper) Harvets Harvets Harvets Harvets Harvets Potato Potato Ryegrass Potato Potato M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F 1 Year Rotation 2 and 3 Years Rotation 4 Year Rotation Conservational tillage (CT) - Scenario

8 Conservational Tillage (CT) Measuring impact of conservation tillage for potato crop Experimental plots were installed in 2011 by CIAT in CT and IT systems Soil losses N and P in runoff A B A) Intensive Tillage (IT) B) Conservational Tillage (CT)

9 Input Data Watershed level Experimental field plots DEM CAR 30m Land Use IGAC - 1: Soil profile descriptions in each plot Lab soil analysis (Bulk density, Soil available water content, Hydraulic conductivity, % carbon, % sand, % silt, % clay and OM) Chemical parameters (P, NH4 and NO3) at the soil profile Soils IGAC - 1: Weather stations CAR / IDEAM St. Number: 21 Fuquene Watershed Plot Location

10 SWAT set up ArcSWAT Version _1.15 For ArcGIS 10.1 released 6/20/14 revision 627 was used jointly by SWAT 2012 Daily rainfall, temperature and relative humidity data 5 slope ranges (0-5%; 5-15%; 15-25%; 25-45%; and >45%) Hargreaves method used for potential evapotranspiration (PET) 31 drainage areas (sub-basins) were defined 4696 HRU defined Combinations of sub-basin, land use, soil and slope 170 HRUs were selected because these spatially correspond with the plots installed in the field

11 SWAT set up Parameter values related management practices per scenario Variable Name Value IT CT POTA PASTURE POTA OAT PASTURE Planting PLANT_ID Plant/land cover code from crop.dat POTA RYEG POTA OATS RYEG HEAT UNITS PHU: Total heat units required for plant maturity BIO_INIT Initial dry weight biomass (Kg/ha) HI_TARG Target harvets index BIO_TARG Biomass (dry weight) target (metric tons/ha) CN2 Initial SCS runoff curve number (Min 35- Max 98) Grazing MANURE_ID Manure code from fert.dat Beef-Fresh Urea GRZ_DAYS Number of days of grazing BIO_EAT Dry weight plant biomass consumed daily (kg/ha) BIO_TRMP Dry weight of biomass trampled daily ((Kg/ha)/day) MANURE_KG Amount of manure applied -dry weight (kg/ha) 6 6 BIO_MIN Minimum plant biomass for grazing to occur (kg/ha) Tillage TILLAGE_ID Tillage implementation Bedder shaper Rotovator-bedder Chisel Plow Gt2ft -vertical Bedder shaper EFFMIX Mixing efficiency of tillage operation (fraction) DEPTIL Depth of mixing by tillage operation (mm) BIOMIX Biological mixing efficiency (fraction) Fertilizer FERT_ID Type of fertilizer/manure applied Urea FRT_KG Definition Amount of fertilizer/manure applied (Kg/ha) 1400 (2 times of 700 each one's) 1000 (2 times of 500 each one'sl) FRT_ SURFACE Fraction of fertilizer applied to top 10 mm

12 Flow calibration and validation Parameters included in the flow calibration process and their final calibration values Parameters Description in SWAT Range Model default value ALPHA_BF Baseflow alpha factor [days] GW_DELAY Groundwater delay [days] GW_REVAP Groundwater "revap" coefficient RCHRG_DP Deep aquifer percolation fraction REVAPMN Threshold water depth in the shallow aquifer for "revap" [mm] GWQMN Threshold water depth in the shallow aquifer for flow [mm] SHALLST Initial depth of water in the shallow aquifer (mm) GW_SPYLD Specific yield of the shallow aquifer (m3/m3) Final value GWHT Initial groundwater height [m] CN2 Initial SCS CN II value Changes conducted for each HRU SOL_K Saturated hydraulic conductivity [mm/hr] Changes conducted for SOL_AWC Available water capacity [mm H20/mm soil] 0-1 each Soil Survey Unit

13 Flow calibration and validation Catchment station CALIBRATION ( ) VALIDATION ( ) Flow rate (m 3 /s) NSE Flow rate (m 3 /s) Sim. Obs. Sim. Obs. NSE La Boyera El Pino Pte. La Balsa Nash Sutcliffe coefficient of efficiency (NSE) NSE ranges between - and 1.0. NSE=1 being the optimal value NSE 0 unacceptable performance Pte. Colorado Observed Simulated Observed Simulated Flow rate (m3/s) Flow rate (m3/s) Simulated and observed flow rate in the La Boyera station and El Pino stations respectively

14 Sediment and nutrient calibration and validation Parameters included in the sediment and nutrient calibration and their final values Parameters Description in SWAT Location Range Sediment Default value BIOMIX Biological mixing efficiency.mgt CN2 SCS runoff curve number for moisture condition II.mgt % USLE_P USLE equation support practice practices.mgt SLSUBBSN Average slope length..hru Changes for HRU - 10% Crop growth T_OPT Optimal temp for plant growth..dat T_BASE Min temp plant growth..dat HEATUNITS Total heat units for cover/plant to reach maturity.mgt * Nutrients PHOSKD Phosphorus soil partitioning coefficient..bsn NPERCO Nitrogen percolation coefficient..bsn RSDCO Residue decomposition coefficient.bsn SOL_LABP Initial (soluble) P concentration in surface soil layer [mg/kg].chm SOL_NO3 Initial NO3 concentration in the soil layer [mg/kg].chm SOL_ORGN Initial organic N concentration in the soil layer [mg/kg].chm SOL_ORGP Initial organic P concentration in surface soil layer [mg/kg].chm PPERCO_SUB Phosphorus percolation coefficient..chm BIO_TARG Biomass (dry weight) target (metric tons/ha).mgt FRT_SURFACE Fraction of fertilizer applied to top 10mm of soil.mgt Final value * Value calculated with local weather using PHU_program available at SWAT webpage (

15 Sediment and nutrient calibration and validation Sediment and nutrient losses performance Variable ** IT Observed Simulated Ɛ* Observed Simulated Ɛ CT Surface runoff (l/m2) NO3 in surface runoff (kg N/ha) Soluble P (kg P/ha) Sediment Yield (T/ha) **Accumulate total values for calibration period ( September March 201 ). * Ɛ: Relative error. Surface runoff (l/m2) IT CT Sediment Yield (T/ha) IT CT NO3 in surface runoff (kg N/ha) IT CT Soluble P yield (kg P/ha) IT CT Observed Observed Observed Observed Simulated Simulated Simulated Simulated Surface runoff and Sediment yield decrease Simulated values were underestimated Nitrate in surface runoff and Soluble P increase Soluble P value was overestimated

16 Effect on potato crop yield Average biomass and yield potatoes measured by CIAT team for IT at the runoff plots Potato variety Total Yield (Ton/ha) Yield (Ton/ha) Dry weight Humidity Potatoes (%) Total Biomass (Ton/ha) Dry weight ICA-UNICA BETINA Average Average yield potatoes by SWAT model at HRU level Good dry matter yield calibration (5.3 ton/ha SWAT model at IT system and 5.8 ton/ha measured) Yeraly dry matter for potato (Tons/ha) Baseline - Yield Scenario 1 - Yield Baseline - LAI Scenario 1 - LAI CT = 4.5 IT = 5.3 Sep-11 Oct-11 Nov-11 Dec-11 Jan LAI ( -) Average dry matter yield from IT is half ton more than CT (4.5 ton/ha) LAI and dry matter development for potato crop at 2141 HRU Baseline and Scenario 1

17 Summary results Effects of the CT system at HRU level according to SWAT simulations Variable Scenario 1 (IT) Scenario 2 (CT) Reduction (%) Surface runoff (l/m2) Sediment Yield (T/ha) Nitrogen Losses (kg/ha) Total N Loss Organic N Nitrate Surface Runoff Nitrate Leached Nitrate Lateral flow Nitrate Groundwater Yield Phosphorus Losses (kg/ha) Total P Loss Organic P Soluble P Surface Runoff CT reduce surface runoff by 27% CT reduce sediments yield by 45% CT reduce Organic P by 26% CT increase total N and P (17% and 28% respectively)

18 Effect on the CT extrapolation at the basin level Effect on the CT extrapolation scenario for the entire potato crop in the Fuquene basin Variable Scenario 1 (IT) Scenario 2 (CT) Reduction (%) Reduction at HRU level (%) Surface runoff (mm/yr) Sediment Yield (T/ha) Nitrogen Losses (kg/ha) Total N Loss Organic N Nitrate Surface Runoff Nitrate Leached Nitrate Lateral flow Nitrate Groundwater Yield Phosphorus Losses (kg/ha) Total P Loss Organic P Soluble P Surface Runoff Summary of results of the main effects of the IT and CT system on N and P total losses at watershed level

19 Conclusions Results suggest that CT at watershed level reduces 26% sediment yield and 11% surface runoff compared with IT, which means an overall reduction of load. The main CT effect on nutrient losses in runoff is an increase in the total N and P (2% to 18% respectively) compared to baseline. The results at watershed scale showed different patterns than the ones obtained at HRU (calibrated and validated). Additional study needs to be carried out in order to make an appropriate extrapolation of CT.

20 Recommendations An adjustment to the amounts of fertilizer could help decrease nutrients in runoff of CT in potato, considering that green manure made an important nutrients contribution. Modeling approach can be used to estimate the impacts of CT-BMPs, however, it is necessary to have data for others runoff plots in different type of soils.

21 Thank you. Natalia Uribe Time for questions

22 Effect on nutrients (N and P) Annual nitrogen mass balance at HRU level Baseline (left) and CT (right) Rainfall 26.8 Fertilizer 364 Rainfall 26.8 Fertilizer 229 Soil Profile Denitrification 0 Mineralization 46.6 NO 3 Nitrification x Plant uptake Surface runoff = 0.4 Lateral flow = 4.0 Leached flow = Total Organic yield 0.08 Soil Profile Denitrification 0 Mineralization NO 3 Nitrification X Plant uptake Surface runoff = 0.5 Lateral flow = 4.8 Leached flow = Total Organic yield 0.12 Shallow acquifer storage Groundwater flow = 50.3 River Unit: kg/ha Input Output Shallow acquifer storage Groundwater flow = 61.4 River Unit: kg/ha Input Output Available N at the surface due to residual green manure increase Nitrate Surface Runoff by 19% Percolation increase by 29% Soil water content increase by 3% Mineralization increase by 240% (transformation of fresh N organic to mineral N, which allows to be released)