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IRRIGATION AND DRAINAGE

Irrig. and Drain. 63: 538–549 (2014) Published online 14 January 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ird.1822

AUTOMATIC CALIBRATION OF A HYDROLOGIC MODEL FOR SIMULATING GROUNDWATER TABLE FLUCTUATIONS ON FARMS IN THE EVERGLADES AGRICULTURAL AREA OF SOUTH FLORIDA† HOYOUNG KWON1*, SABINE GRUNWALD2, HOWARD BECK3 AND YUNCHUL JUNG3 1

International Food Policy Research Institute (IFPRI), Environment and Production Technology Division, NW Washington District of Columbia, USA 2 University of Florida, Soil and Water Science Department, Gainesville, Florida, USA 3 University of Florida, Department of Agricultural and Biological Engineering, Gainesville, Florida, USA

ABSTRACT The Shuffled Complex Evolution—Universal Algorithm (SCE-UA) is an automatic calibration algorithm that has shown success in finding a globally optimum objective function with more efficiency than other methods. We incorporated the SCE-UA into our novel modeling environment, utilizing an ontology-based simulation (OntoSim-Sugarcane) framework adapted to analyze groundwater table (WT) fluctuations and drainage practices on four farm basins in the Everglades Agricultural Area of south Florida. Utilizing two water years (WY96–97) of farm WT fluctuations observed at a portion ( 20 cm h
1) (Bottcher and Izuno, 1994) and the flat topography within the farms.

CONCLUSIONS To simplify the calibration process to enable farmers and practitioners to use OntoSim-Sugarcane, we incorporated the SCE-UA as an automatic calibration algorithm into the model. This coupling facilitates model parameters to be automatically and objectively adjusted to provide the best fit between observed and simulated data for a particular basin or watershed. In this study, we demonstrated the application of the SCE-UA for the purpose of analyzing WT fluctuations in farms of the EAA by optimizing site-specific parameters describing vertical and lateral water fluxes. When recalibration is necessary because parameters have different values in different farms even in an area such as the EAA which is relatively homogeneous, this can be accomplished now using SCE-UA. This will strengthen the model’s capability to simulate hydrologic and nutrient dynamics by objectively finding the best parameter sets in multiple farm basins in support of accurate hydrologic and water quality predictions. This will further guide optimization of water management practices and recommendations to improve water quality.

ACKNOWLEDGEMENTS This project is supported by grant WM837 from the Florida Department of Environmental Protection. The authors are very grateful to Dr Samira H. Daroub and Timothy A. Lang for providing farm basin data.

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Irrig. and Drain. 63: 538–549 (2014)