This study evaluated the TBET model for improving P indices in southern states. TBET predictions were compared to measured P loss data from Arkansas, Georgia, and North Carolina. The uncalibrated model performed poorly but predictions improved with calibration, especially for runoff. Calibrated TBET and the NC P Index had similar accuracy for annual P loss predictions. While TBET could potentially improve P indices, calibrating it for each state would be time-consuming. Overall, TBET shows promise for predicting P losses but needs work to better estimate sediment and dissolved P.

1. Evaluation of the TBET model for improving P indices
in Southern states
David E Radcliffe, University of Georgia
Adam Forsberg, CH2M Hill, Atlanta, GA
Carl H. Bolster, USDA‐ARS, Bowling Green, KY
Deanna Osmond, North Carolina State University

2. CIG Background
• P indices vary widely among states
• Data to test P indices are limited
• Can simple field‐scale models be used to test and improve P indices?

3. TBET: Texas Best Management Practice
Evaluation Tool
• Developed to be used as a P‐Index
in Texas by White et al. (2010,
2012)
• Field‐scale version of Soil Water
Assessment Tool (SWAT)
• Basin‐scale parameters were
calibrated using 20 streams by
White et al. (2012)
• Field‐scale parameters can be
calibrated for sites or regions
• Designed to be easy to use by
conservation planners and farmers
• Predicts runoff and losses of
sediment, N, and P

4. Objective
• To evaluate the accuracy of TBET and determine whether it could be
used to improve P Indices for states in the southern region
• Using event‐based measured P loss from previous studies in AR, GA, and NC
• Using annual averages of measured P loss from a previous study in NC and
compare it with NC P Index

5. TBET User Interface
Required Inputs
• Climate
Daily rainfall & max/min
temperature
• Soils
Up to 3 series
• Land use
Cropping system
• Topography
Field area
Field slope
• Soil Test P
Mehlich III
• Fertilizer & manure applications

6. Study Sites
Site
Field size Duration Crop
Annual
rainfall
Annual
runoff
Annual
erosion
Soil test P
ha yrs cm cm Mg ha-1 mg kg-1
AR 0.4 3 Pasture 98 – 137 0.3 – 19 0.007 – 0.2 81 – 183
GA 0.72 – 0.79 4 Pasture 101 – 118 5 – 33 < 1 14 – 142
NC 0.04 3
Corn with
wheat
cover
80 – 157 3 – 83 0.05 – 8 41 – 121

7. Calibration and Validation Periods
Site # Fields Years Site-years
Uncalibrated,
Calibration
Washington Co., AR 7 2009, 2010 14
Putnam Co., GA 6 1995, 1998 12
Henderson Co., NC 5 2011, 2012 10
Validation
Washington Co., AR 7 2011 7
Putnam Co., GA 6 1996, 1997 12
Henderson Co., NC 5 2013 5

8. Model process
• Single year simulations run on a daily time‐step
(1/1/YYYY – 12/31/YYYY)
• 2 years of warm‐up
Initialize soil‐moisture profile and nutrient pools
• Model evaluation
Nonparametric regression Kendall‐Theil Robust Line (KTR) method
Nash‐Sutcliffe Efficiency (NSE) primary criteria
NSE >0.30 for daily time step considered “successful”
Developed prediction intervals to measure uncertainty

9. Calibration Parameters
Variable Description Calibration range
Runoff Curve number (-16, -8, -4, -2, 0, +2, +4, +8, +16)
Erosion; sediment
bound P
USLE soil erosion factor (-60%, -40%, -20%, 0, +20%, +40%, +60%)
USLE crop factor (-60%, -40%, -20%, 0, +20%, +40%, +60%)
Peak rate adjustment factor (0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75)
Subbasin slope length (-60%, -40%, -20%, 0, +20%, +40%, +60%)
Dissolved P P percolation coefficient (5, 7.5, 10, 12.5, 15)
Soil P partitioning coefficient (100, 125, 150, 175, 200)

10. Overview of Results
• Event‐based predictions for AR, GA, and NC
• Uncalibrated model performed poorly
• Predictions improved with calibration
• Runoff predictions were successful (NSE ≥ 0.3) in 2 of 3 states during calibration and in
all 3 states during validation
• Erosion predictions were still poor
• Dissolved P predictions under‐estimated losses and could be due to lack of manure pool
• Total P predictions better but a matter of getting the right answer for the wrong reasons
in AR and GA
• Over‐predicting sediment loss and under‐predicting dissolved P loss resulted in reasonably
good predictions of total P loss
• Annual predictions for NC
• Calibrated TBET and P Index had about the same accuracy
• Will show the results for event‐based validation runs and annual
predictions

11. Statistic†
Uncalibrated Calibration Validation
AR GA NC AR GA NC AR GA NC
Runoff
MAE, mm 20.54 9.74 6.72 13.82 9.33 6.8
18.6
6
6.05 6,89
NSE -7 0.57 0.23 -2 0.58 0.33 0.35 0.57 0.65
PBIAS, % -179 -48 -39 -112 -48 -6 -81.1 -40.1 -41.8
Sediment
MAE, Mg
ha-1
0.02 - 0.53 0.01 - 0.07
0.01 - 0.13
NSE -59 - -88 -0.57 - -0.67 0.24 - 0.31
PBIAS, % -256 - -963 53 - 16 39.4 - 12.2
Total P
MAE, kg P
ha-1
0.17 0.35 0.89 0.12 0.30 0.17
0.25 0.59 0.22
NSE -1.5 0.34 -78 0.01 0.51 -1.7 0.27 -0.02 0.35
PBIAS, % -55 20 -654 34 10 9 56.8 69.5 -16.9
Dissolved P
MAE, kg P
ha-1
0.13 0.32 0.01 0.1 0.29 0.01
0.24 0.42 0.04
NSE -1.6 0.19 -0.08 -0.14 0.35 0.21 0.27 0.38 0.32
PBIAS, % -35 11 -21 24 2 15 59.9 57.6 46.1
†MAE , mean absolute error; NSE, Nash-Sutcliffe efficiency; PBIAS,
percent bias.

12. Event‐Based Runoff

13. Event‐Based Sediment

14. Event‐Based Dissolved P

15. Event‐Based Total P

16. Annual Predictions for NC

17. Summary
• Uncalibrated model performed poorly in predicting event‐based
losses
• Runoff predictions with calibrated model were pretty good, but
predictions of sediment were poor and dissolved P was under‐
predicted
• Calibrated TBET and P Index had about the same accuracy in
predicting annual losses
• We think a calibrated TBET model could be used to accurately predict
annual P losses in southern states and to improve or replace P Indices
• However, calibrating for each state would be very time‐consuming
• 2‐3 years to set up the weather, soil, and management files required for all of
the practices within a state and to calibrate the model
• Would be important to include calibration of the basin‐scale parameters in
each state as was done by White et al. (2012)