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Effect of Parameter Uncertainty on DRAINMOD Predictions: I. Hydrology and Yield

Haan, P.K., Skaggs, R.W.
Transactions of the ASAE 2003 v.46 no.4 pp. 1061-1067
DRAINMOD model, Monte Carlo method, crop yield, field experimentation, growing season, model uncertainty, parameter uncertainty, prediction, saturated hydraulic conductivity, soil water, subsurface drainage, uncertainty analysis, variance, North Carolina
The computer-based hydrologic model DRAINMOD can be used to predict the effect of drainage design on the rate of subsurface drainage and on crop yield. An uncertainty analysis was conducted to quantitatively assess the variability in model outputs caused by parameter uncertainty. The analysis was based on an experimental field at the Tidewater Research Station in Plymouth, North Carolina. As a first step in the uncertainty analysis, a sensitivity test was conducted to determine which parameters in the model have the most influence on the model objective functions. First-order approximation and Monte Carlo simulation were used to determine the effect of the uncertainty in the most sensitive parameters on the uncertainty in the model objective functions. Objective functions evaluated were: average annual subsurface drainage volume; SEW 30 (a measure of stress caused by excessive soil water in the top 30 cm) during the growing season; and relative yield for both conventional and controlled drainage. Nine parameters found to significantly affect model output were used in the uncertainty analysis. The first-order approximation showed that in the case of conventional drainage, lateral saturated hydraulic conductivity accounted for 81% of the uncertainty in terms of variance for predicted annual subsurface drainage volume, 81% for growing season SEW 30 , and 71% for relative yield. For controlled drainage, lateral saturated hydraulic conductivity contributed 62% of the uncertainty in terms of the variance in predicted annual subsurface drainage volume, 69% in growing season SEW 30 , and 62% in relative yield. The Monte Carlo simulation showed similar results. Improving the knowledge of these most influential parameters will help to reduce the uncertainty in DRAINMOD predictions for these objective functions.