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Enhancing drainage modeling capabilities in Brazil and the United States


Drainage is an important aspect of production agriculture both in Brazil and the United States. Simulation models are frequently used for drainage design or to characterize system behavior. The most frequently used model for subsurface drainage design in the US and much of the world is DRAINMOD. This model essentially provides an hourly water and chemical balance in the soil, midway between two parallel drains, but has been adapted for field and watershed applications. It also estimates yield loss due to excess or deficit water stress, and weather-related planting delays. DRAINMOD has been shown to be very useful in predicting water table responses, and surface and subsurface discharges. The model has been successful in various regions of the world, with different soils, crops and management practices. The SISDRENA model was developed in Brazil. It provides a daily water balance in the soil, midway between two parallel drains. Like DRAINMOD, it provides relative yield estimates. It also includes an economic analysis component that can be used to make decisions about the profitability of a proposed drainage system. The long-term goals of this collaboration are 1) to develop expertise and data sets for the application of DRAINMOD in Brazil, and 2) to make SISDRENA more accessible to researchers and drainage practitioners in the US. I propose to do a workshop on DRAINMOD in which, at a minimum, the following topics will be covered; Preparing soil input files from moisture release data; Preparing weather input files; Introduction to the DRAINMOD interface; Parameter selection - sensitivity analysis; Parameter selection – calibration; Modeling controlled drainage with DRAINMOD; DRAINMOD Simulation of Wastewater Treatment Systems also propose to work with Dr. Jarbas de Miranda to start the development of an English language interface for SISDRENA. This interface will make the model accessible to a wider audience. Over the course of our collaboration, we will run both models on data sets from Brazil and Illinois, and compare the results from both locations. (AU)

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