SWAT 2015 International Conference:

Transcription

1 SWAT 2015 International Conference: Comparative Analysis of Spatial Resolution Effects on Standard and Grid-based SWAT Models Presented by: Garett Pignotti Co-authors: Dr. Hendrik Rathjens, Dr. Cibin Raj, Vamsi Vema, Dr. Indrajeet Chaubey, & Dr. Melba Crawford 1

2 2 Outline I. INTRODUCTION II. BASELINE MODEL CALIBRATION Resolution III. RESOLUTION EFFECTS ON: i. SIMULATIONS ii. CALIBRATION IV. CONCLUSIONS & FUTURE EFFORTS Simulations Calibration Discretization Routing

3 Landscape Representation in SWAT Hydrologic Response Unit = HRU 3

4 4 SWATgrid Gridded Input Modified Routing SWATgrid Raster-based Delineation = Grid-based Interface (Rathjens & Oppelt 2012) Topaz (Garbrecht & Martz 1997) Landscape routing with SWATgrid (Rathjens et al. 2014) Landscape routing (Arnold et al. 2010)

5 5 Watershed Routing Standard Landscape

6 6 Research Questions & Objectives 1. What is the effect of resolution on output of both models? Identify a resolution for SWATgrid that both maximizes prediction accuracy while minimizing computation time 2. How do simulations of SWATgrid compare to standard HRU implementation? Compare model output 3. How do calibration parameters change with respect to resolution? Discriminate parameters sensitive to resolution change

7 7 Methods Study Site Cedar Creek Watershed 700 km 2

8 8 Methods Study Site DATA SOURCES: ELEVATION (30 M NED) LAND COVER (30 M NASS CDL) SOILS (250 M STATSGO) RESAMPLING: NEAREST NEIGHBOR (LU & SOIL) CUBIC CONVL. (DEM)

9 BASELINE MODEL CALIBRATION 9

10 10 Methods Resolution Effects 30 m Input Data 30 m Calibrated HRU Model AMALGAM (Vrugt et al. 2007)

11 11 Methods SWAT Modeling Management Strategy Jan 15 - Apr 22 N App. 1 Apr 22 Atrazine 2 Corn Year Soybean Year May 6 Cultivator May 6 Planting Jun 6 N App. 3 Oct 14 Harvesting Oct 15 Killing May 24 Zero Till May 24 Planting Oct 7 Harvesting Oct 8 Killing Oct 15 P App. 4 Nov 1 Chisel 1: Anhydrous of 53 kg/ha (N of 43 kg/ha); 2: Atrazine of 2.2 kg/ha; 3: Urea of 284 kg/ha (N of 131 kg/ha); 4: DAP (P 2 O 5 ) of 123 kg/ha (P of 54 kg/ha). Dominant management practices Tile Drainage Model Setup Warm up: Calibration: Validation:

12 12 Basin Level Parameters Parameter Definition Units Range Default Calibrated SFTMP snowfall temperature o C SURLAG surf. runoff lag coefficient day SMTMP snow melt base temperature o C TIMP snow pack temp. lag factor SMFMX SMFMN maximum melt factor minimum melt factor mm H 2 0/ o C mm H 2 0/ o C

13 13 Calibration Results Nash-Sutcliffe Efficiency: USGS Cedar Creek near Cedarville, n IN Obs i Sim 2 i NSE = 1 i=1 n i=1 Obs i Sim 2 Calibration Validation Monthly NSE Obs. Mean (cms) Sim. Mean (cms) Monthly Flow (cms)1000 Observed Simulated 0

14 SIMULATION EFFECTS 14

15 Methods Simulation Effects 30 m Input Data 30 m Calibrated HRU Model Parameter Transfer Same Version (rev. 574) HRU model AMALGAM (Vrugt et al. 2007) GRID model Landscape & Standard Resolutions tested: 30, 60, 90, 150, 250, 500, & 1,000 m 15

16 Watershed Area Tends to Decrease with Increasing Resolution Watershed Area (km 2 ) m 60 m 90 m 150 m m m m HRU GRID Resolution (m)

17 Watershed Area Land Use & Soil Distributions by % Area Remain Relatively Constant 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Corn Soybean IN004 General Agr. IN005 Barren IN007 Range/Brush IN016 Pasture IN019 Forest IN025 Urban IN028 Wetland Water 0% Input Input Data Data Resolution (m) (m) 17

18 21 Year Total Simulation Time (mins) Simulation Time Exponentially Increases with Finer Resolution Models Intel Core i GHz 4 GB RAM 30 & 60 m GRID Models Not Possible 86,635 cells 27,253 cells ,916 cells 10 2,451 cells 385 cells 1 GRID1000 GRID500 GRID250 GRID150 GRID90

19 Monthly Flow (cms) Monthly Flow at the Outlet is Under Predicted by the GRID Models HRU150 GRID-LAND GRID-STD

20 Monthly Flow (cms) GRID Models Under Predict High Flows HRU GRID - LAND GRID - STD Observed HRU 30 HRU 60 HRU 90 HRU 150 HRU 250 HRU 500 HRU 1000 Observed GRID 90 GRID 150 GRID 250 GRID 500 GRID Exceedance Probability 20

21 Avg. Annual Flow (cms) % Relative Error Average Annual Flow Follows Similar Trend but Different in Magnitude 60 HRU 50 GRID-LAND 40 GRID-STD Resolution (m) 21

22 Simulate Monthly Avg. Flow NSE (cms) Watershed Area Impacts Predictions HRU HRU GRID-LAND GRID-STD Watershed Area(km 2 ) 2 ) 22

23 Subsurface Flows are Divergent between Models (150 m Comparison) 23 Relative Average Diff. Average Annual Basin Annual Values Basin (mm) Values (mm) PRECIP SNOW FALL SNOW MELT SUBLIMATION SURFACE RUNOFF Q LATERAL SOIL Q TILE Q GROUNDWATER (SHAL AQ) Q GROUNDWATER (DEEP AQ) Q REVAP (SHAL AQ > SOIL/PLANTS) DEEP AQ RECHARGE TOTAL AQ RECHARGE TOTAL WATER YLD PERCOLATION OUT OF SOIL ET PET TRANSMISSION LOSSES GRID-LAND GRID-STD HRU GRID-LAND GRID-STD

24 GRID Models Capture Spatial Hydrology 24

25 CALIBRATION EFFECTS 25

26 26 Methods Calibration Effects Input Data Unique Parameters HRU model AMALGAM (Vrugt et al. 2007) HRU model HRU model

27 Converged Monthly Monthly NSE NSE Optimized Objective Function Value Decreases with Coarser Resolutions Simulation Resolution (m) 30 m 90 m 60 m 150 m 250 m 500 m 1000 m 27

28 Parameter Distributions Similar Up to 90 m 28

29 29 Summary & Concluding Remarks I. Lower flow for GRID models relative to calibrated HRU: II. Discretization & routing effects Implicit restriction for simulations: Number of grids = f(input resolution, watershed area) III. Potentially possible to scale to 90 m in this study IV. Use of SWATgrid best for specific applications FUTURE EFFORTS I. More rigorous analysis of discrepancies between models II. Test sensitivity of flow separation index III. Test in other watersheds

30 Thank You! 30