Subsurface Drainage Volume Research Articles

STATISTICAL METHOD FOR EVALUATION OF A WATER TABLE MANAGEMENT MODEL

Evaluations of Hydrologic/Water Quality (H/WQ) models have been largely based on the comparison of modelresults with observed data for a particular site-specific location. However, this procedure is usually subject to extensiveparameter estimation within the simulation model to best fit the model results to the observed data. In direct contrast to thisapproach, methods developed by Haan et al. (1995) utilize input probability distributions to transform parameteruncertainty into prediction uncertainty using probability distribution functions. In this study, a procedure is introduced forapplying the statistical approach defined by Haan et al. (1995) to water table management models such as DRAINMOD, aH/WQ model used to simulate lateral and deep seepage through the soil profile. In the evaluation procedure, probabilitydistribution functions were developed for the most sensitive input parameters, output probability distribution functions weredeveloped using Monte Carlo simulation, and the output probability distribution functions were used to assess the model.The model was evaluated based on its capability to predict average annual runoff volume, subsurface drainage volume, anddaily fluctuations of water table depth. DRAINMOD performed successfully in the evaluative procedure in predicting therunoff and subsurface drainage for the research site. Furthermore, water table depth fluctuations, which were expected tobe most susceptible to input uncertainty, were also predicted accurately by the model.

Using the DRAINMOD-N model to study effects of drainage system design and management on crop productivity, profitability and NO 3–N losses in drainage water

The environmental impacts of agricultural drainage have become a critical issue. There is a need to design and manage drainage and related water table control systems to satisfy both crop production and water quality objectives. The model DRAINMOD-N was used to study long-term effects of drainage system design and management on crop production, profitability, and nitrogen losses in two poorly drained soils typical of eastern North Carolina (NC), USA. Simulations were conducted for a 20-yr period (1971–1990) of continuous corn production at Plymouth, NC. The design scenarios evaluated consisted of three drain depths (0.75, 1.0, and 1.25 m), ten drain spacings (10, 15, 20, 25, 30, 40, 50, 60, 80, and 100 m), and two surface conditions (0.5 and 2.5 cm depressional storage). The management treatments included conventional drainage, controlled drainage during the summer season and controlled drainage during both the summer and winter seasons. Maximum profits for both soils were predicted for a 1.25 m drain depth and poor surface drainage (2.5 cm depressional storage). The optimum spacings were 40 and 20 m for the Portsmouth and Tomotley soils, respectively. These systems however would not be optimum from the water quality perspective. If the water quality objective is of equal importance to the productivity objective, the drainage systems need to be designed and managed to reduce NO 3–N losses while still providing an acceptable profit from the crop. Simulated results showed NO 3–N losses can be substantially reduced by decreasing drain depth, improving surface drainage, and using controlled drainage. Within this context, NO 3–N losses can be reduced by providing only the minimum subsurface drainage intensity required for production, by designing drainage systems to fit soil properties, and by using controlled drainage during periods when maximum drainage is not needed for production. The simulation results have demonstrated the applicability of DRAINMOD-N for quantifying effects of drainage design and management combinations on profits from agricultural crops and on losses of NO 3–N to the environment for specific crop, soil and climatic conditions. Thus, the model can be used to guide design and management decisions for satisfying both productivity and environmental objectives and assessing the costs and benefits of alternative choices to each set of objectives.

GLEAMS-WT Hydrology Submodel Modified to Include Subsurface Drainage

The model GLEAMS-SWAT (GLEAMS with Subsurface drainage and WAter Table) is a modified version of GLEAMS that accounts for shallow water table fluctuations and subsurface drainage. The modification was accomplished by incorporating a subsurface drainage routine in GLEAMS-WT. Simulation performances of GLEAMS and GLEAMS-SWAT were evaluated by comparing their predictions with seven years (1981-1987) of measured data from a runoff-erosion-drainage experimental plot at Baton Rouge, Louisiana. Validations to test the accuracy of GLEAMS-SWAT predictions of surface runoff volume, subsurface drainage volume, total volume (surface runoff + subsurface drainage), and water table depth were satisfactory. Total predicted surface runoff volume for the seven-year period was 94% of the observed runoff volume, an improvement from GLEAMS underprediction of surface runoff volume which was 71% of the observed runoff. Subsurface drainage volume and total drainage (runoff + subsurface drainage) volume predictions were, respectively, 99% and 96% of the observed volumes. Water table depth prediction was deeper than the observed depth, especially during the pregrowing and growing seasons.