STREAM FLOW MODELING IN THE NACUNDAY RIVER BASIN (PARAGUAY, SOUTH AMERICA) USING SWAT MODEL. Sandra Mongelos and Manoj K. Jain

Transcription

1 STREAM FLOW MODELING IN THE NACUNDAY RIVER BASIN (PARAGUAY, SOUTH AMERICA) USING SWAT MODEL Sandra Mongelos and Manoj K. Jain DEPARTMENT OF HYDROLOGY INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE (INDIA) JULY, 2012

2 Hydrological Models WEPP(Water SWAT (Soil Erosion and Water Assessment Tool) Prediction Project) HSPF User-friendly (Hydrologic Simulation Program Fortran) ARC VIEW Interface SHETRAN(System Hydrologique Realistic representation of Europeen-TRANsport) spatial variability of catchment characteristics SWAT (Soil and Water Assessment Free-availability Tool)...availability and user-friendliness in handling input data (Arnold et al.,1998)... is a model coupled with geographic information system (GIS). The principal advantage of such a model is that it can realistically represent the spatial variability of catchment characteristics (Mishra et al., 2007). Developed and maintained by the USDA

3 SWAT in South America BRAZIL :from 1999 to 2010, over 70 publications such as theses, dissertations and articles about the use of the model in Brazilian watersheds were identified. URUGUAY : simulation of hydrologic response of two catchments during the pretreatment period and prediction of the hydrologic effects of converting PARAGUAY the native No studies pasture using to pine SWAT plantation. Model (VON were Stackelberg identified. et al. 2007). ARGENTINA SWAT model was applied to calibrate and validate stream flow in an agricultural micro basin in the Pampa Ecoregion, with successful results on daily basis and poor results on monthly basis (Behrends et al. 2011).

4 Objectives of the work 1. Sensitivity analysis of the 1. Sensitivity analysis of the parameters assumed in the model parameters assumed in the model 2. Calibration of the parameters of the considered parameters in the model. considered in the model. 3. Validate the performance of SWAT and the feasibility of using this model 3. Validate the performance of SWAT and the feasibility of using this model as a as simulator a simulator of runoff of at runoff a catchment at scale a catchment in the sub tropical region of Nacunday river catchment area. scale in the sub tropical region of Nacunday river catchment area.

5 Study Area: NACUNDAY BASIN Area: 2430 km2 Elevation: msl (ranging flat to gently slope) Climate: Humid subtropical climate (Cfa)

6 SPATIAL DATA ARC SWAT INPUT DATA TREE CORE NON-SPATIAL DATA OPTIONAL DATA DEM LAND USE MAP SOIL MAP WEATHER STATIONS LOCATIONS POINT SOURCES WATER QUALITY HYDRO- GEOLOGY WATER USES SOILS CLIMATE MANAGEMENT PRACTICE Chemical data: Soil NO3, Soil Org N, Soil P, Soil Org. Physical data: Ksat, Bulk density, Texture, AWC, USLE K, Org C, Rock fragm., Soil Hydrologic Group Weather: Max Temp., Min Temp., Solar radiation, Wind speed, Relative Humid. Precipitation: Rainfall, Snow Planting date, Harvest date, crop rotation, Fertilizer, Pesticide, Irrigation, Tillage Information

7 DEM Digital Elevation Map Global SRTM data ( 8080/esdi/index.jsp) 90-m resolution Geographic coordinate system: GCS_WGS_1984, Projected to: WGS_1984_UTM_ZONE_21S

8 LAND USE MAP and 2009 Landsat-5 Thematic Mapper images 30-m spatial resolution Geographic coordinate system: GCS_South_American_1969. Reclassification Projected to : WGS_1984_UTM_ZONE_21S 2009

9 FAO-UNESCO Soil Map of the World (DSMW) downloaded from the World Soil Information (ISRIC) website ( The spatial data s resolution is 5 by 5 arc-minutes grid (Batjes, 2006) Geographic coordinate system: GCS_South_American_1969 Projected to the WGS_1984_UTM_ZONE_21S SOIL MAP

10 WEATHER STATIONS LOCATIONS Daily Rainfall, Temperature, Relative Humidity From to Barra Station given by the National Agency of Electricity (ANDE) NAME XPR YPR LAT LONG BARRA S W

11 CLIMATE Meteorology data provided by ANDE Daily data for: Rainfall (mm) Temperature (Maximum, Minimum) ( C) Relative Humidity(%) From Jan 1, 1999 to Sept 30, Wind speed and Solar Radiation generated by weather generator. Pcp.dbf Tempe.dbf Rh.dbf Formulas Data

12 SOIL FAO-UNESCO Soil Map ( Two types of soils units were identify in the study area Units are 5614 and 5717 Several physical and chemical parameters of soil layers. OBJECTID MUID SEQN SNAM S5ID CMPPCT NLAYERS HYDGRP SOL_ZMX ANION_EXCL SOL_CRK TEXTURE FAO C CL FAO B FSL-FC SOL_Z1 SOL_BD1 SOL_AWC1 SOL_K1 SOL_CBN1 CLAY1 SILT1 SAND1 ROCK1 SOL_ALB1 USLE_K1 SOL_EC SOL_Z2 SOL_BD2 SOL_AWC2 SOL_K2 SOL_CBN2 CLAY2 SILT2 SAND2 ROCK2 SOL_ALB2 USLE_K2 SOL_EC SOL_Z3 SOL_BD3 SOL_AWC3 SOL_K3 SOL_CBN3 CLAY3 SILT3 SAND3 ROCK3 SOL_ALB3 USLE_K3 SOL_EC SOL_Z4 SOL_BD4 SOL_AWC4 SOL_K4 SOL_CBN4 CLAY4 SILT4 SAND4 ROCK4 SOL_ALB4 USLE_K4 SOL_EC SOL_Z5 SOL_BD5 SOL_AWC5 SOL_K5 SOL_CBN5 CLAY5 SILT5 SAND5 ROCK5 SOL_ALB5 USLE_K5 SOL_EC DEFINITIONS

13 RS IMAGE Image Classification METHODOLOGY GIS DATA LAYERS Formatting into ARC SWAT Land Use map Soil map DEM Overlay for HRU definition Other optional data (Water quality, groundwater, fertilizer, pesticide, plant growth, crop timing & rotation ARC SWAT INTERFACE SWAT RUN VALIDATION Sub watershed delineation Catchment specific databases (soil parameters, weather data) CALIBRATION Changing one at the time parameters SENSITIVITY

14 Nacunday Watershed delineation

15 HRU s definition THRESHOLD=20% THRESHOLD= 5% Over Soil Sub Area Basin Area 83 HRU S THRESHOLD=20% Over Land Use Area

16 For any process studied with SWAT, water balance is the driving force behind what is SW t is the final soil water content (mm H 2 O); SW 0 is the initial soil water content(mm H happening in the 2 O); watershed. t is the time for the simulation period(days); R i precipitation, Q i runoff, Et i evapo-transpiration P i Percolation QR i return flow, on day i(mm H 2 O).

17 Parameters involved in stream flow modeling. PRECIPITATION SURLAG EPCO ESCO CN ALPHA_BF GW_DELAY REVAPMN GW_REVAP CH_N2 CH_K2

18 RELATIVE ORDER OF SENSITIVITY OF PARAMETERS GOVERNING RUNOFF RESPONSE Range Sensitivity Order Parameter code* Lower Upper Sub basin data 1 Alpha_Bf 0 1 *.gw 2 Surlag 0 10 *.bsn 3 Ch_N2 0 1 *.rte 4 Ch_K *.rte 5 Cn *.mgt INITIAL SENSITIVITY 6 Esco 0 1 *.hru,*.bsn 7 Gwqmn *.gw ANALYSIS 8 Sol_Z *.sol 9 Gw_Delay *.gw 10 Sol_Awc *.sol 11 Canmx 0 10 *.hru 12 Blai 0 1 *crop.dat 13 Gw_Revap *.gw 14 Sol_K *.sol 15 Epco 0 1 *.hru,*.bsn 16 Revapmn *.gw 17 Sol_Alb *.sol 18 Slope *.hru 19 Biomix 0 1 *.mgt

19 Monthly runoff Calibration Period CALIBRATION Runoff (mm) GRAPHICAL TECHNIQUE /01 3/01 5/01 7/01 9/01 11/01 1/02 3/02 5/02 7/02 9/02 11/02 1/03 3/03 5/03 7/03 9/03 11/03 1/04 3/04 5/04 7/04 9/04 11/04 1/05 3/05 5/05 7/05 9/05 11/05 Calibration Months (mm/yy) Measured Simulated

20 Daily Runoff Calibration Period SURLAG SOL_K; SOL_AWC; SOL_ALB; 12 CN2 CH_N2 Runoff (mm) 10 8 EPCO ALPHA_BF /1/01 3/1/01 5/1/01 7/1/01 9/1/01 11/1/01 1/1/02 3/1/02 5/1/02 7/1/02 9/1/02 11/1/02 1/1/03 3/1/03 5/1/03 7/1/03 9/1/03 11/1/03 1/1/04 3/1/04 5/1/04 7/1/04 9/1/04 11/1/04 1/1/05 3/1/05 5/1/05 7/1/05 9/1/05 11/1/05 1/1/06 CH_K2 ESCO REVAPMN GW_DELAY GW_REVAP GWQMN Date (dd/mm/yy) Simulated Measured

21 STATISTICAL TECHNIQUE Coefficient of Determination (R 2 ): Nash-Sutcliffe efficiency (NSE): Percent bias (PBIAS):

22 PREFORMANCE RATING Coefficient of Determination (R 2 ): Nash-Sutcliffe Greater than 0.5 efficiency are considered (NSE): Percent bias (PBIAS): acceptable Model (Santhi Value et al., Performance 2001, Van Rating Model Value Performance Liew et al., 2003). Rating SWAT >0.65 Very Good SWAT <10% Very Good SWAT 0.54 to 0.65 Adequate SWAT <10% to <15% Good SWAT >0.5 Satisfactory SWAT <15% to <25% Satisfactory SWAT >25% Unsatisfactory (Moriasi et al.2007).

23 CALIBRATED VALUES OF PARAMETERS Parameters Code* Unit Initial Value Calibrated Value ALPHA_BF days CH_K2 mm/hr CH_N2 n/a CN2 n/a 73,60,85,78 42,65,61 EPCO Fraction ESCO Fraction GW_DELAY days GW_REVAP n/a GWQMN mm REVAPMN mm 1 50 SOL_ALB top layer SOL_Z mm SURLAG days SOL_AWC (mmh2o/mm) 0.05,0.04,0.04,0.05, ,0.2,0.2,0.21,0.25 SOL_AWC (mmh2o/mm) 0.1,0.1,0.1,0.11, ,0.15,0.15,0.17,0.20 SOL_K (mm/hr) 1.5,1,1,0.9, ,4.21,2.34,1.17,0.7 SOL_K (mm/hr) 3.4,1.5,1.3,1.2, ,23.36,17.52,11.68,4.67

24 NSE AND R 2 INDEXES VALUES FOR MODEL EVALUATION IN MONTHLY AND DAILY BASIS. Daily Monthly NSE R2 NSE R2 Calibration Period Validation Period

25 YEARLY PBIAS Year DISCHARGE (mm) SIMULATED (mm) BIAS

26 Monthly runoff Validation Period NSE = 0.61 R2 = 0.67 Runoff (mm) VALIDATION 0.00 Validation Months (mm/yy) Measured Simulated

27 Daily Runoff Validation Period NSE = 0.55 R2 = Runoff (mm) Date (dd/mm/yy) Simulated Measured

28 CONCLUSIONS 1. During the calibration Monthly Daily NSE R2 R2 PBIAS Calibration varied between ±16% which could be performance Satisfactory of the model Calibration considered Period Period as good overall annual performance simulation of the model Very good

29 CONCLUSIONS 2. During the Validation Monthly Daily The PBIAS remained within ±16.5% except for year 2006 where the annual PBIAS of 33.4% NSE NSE was obtained R2 R2 indicating under Satisfactory simulation of performance of the model discharge Validation from Period model Therefore on the basis of PBIAS the model performance Validation can Period be rated as good except for year

30 CONCLUSIONS The results obtained from model are rated acceptable Despite the limitations of: Using large spatial resolution soil data Use of single rain gauge for representing catchment averaged rainfall. The model captured rather well the dynamic of flow generation, with surface runoff uniformly distributed along the year and shallow aquifer contribution during winter s months of July and August.

31 CONCLUSIONS The simulated discharge from SWAT model, both for daily and monthly basis in Nacunday watershed can be rated within acceptable range of errors, so future use of the SWAT model for various scenario testing is reasonable.

32 RECOMMENDATION FOR FURTHER STUDIES Further studies may be undertaken to incorporate field-measured parameters and better representation of soil and rainfall distribution information into the model. Using more field measurements and fewer default values for inputs may provide better opportunity to improve further the SWAT Model s representation of processes in the Nacunday River Basin.

33 THANK YOU