Jan 14, 2014 - AGRICULTURAL AREA OF SOUTH FLORIDA ... Research Institute (IFPRI), Environment and Production Technology Division, NW Washington ...
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 h1) (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.
REFERENCES Agyei E, Hatfield K. 2006. Enhancing gradient-based parameter estimation with an evolutionary approach. Journal of Hydrology 316(1–4): 266–280. Allen RG, Walter IA, Elliott RL, Howell TA, Itenfisu D, Jensen ME, Snyder RL. 2005. The ASCE Standardized Reference Evapotranspiration Equation. American Society of Civil Engineers: Reston, VA. Anat A, Duke HR, Corey AT. 1965. Steady upward flow from water tables. In Hydrology Paper No. 7. Colorado State University: Fort Collins, Colo. Copyright © 2014 John Wiley & Sons, Ltd.
Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment—Part 1: Model development. Journal of the American Water Resources Association 34(1): 73–89. Beck H, Morgan K, Jung YC, Grunwald S, Kwon HY, Wu J. 2010. Ontology-based simulation in agricultural systems modeling. Agricultural Systems 103(7): 463–477. Borst WM. 1997. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. University of Twente. Bottcher AB, Izuno FT. 1994. Everglades Agricultural Area (EAA): Water, Soil, Crop, and Environmental Management. University Press of Florida: Gainesville. Chung SO, Ward AD, Schalk CW. 1992. Evaluation of the hydrologic component of the adapt water-table management model. Transactions of the ASAE 35(2): 571–579. Daroub SH, Lang TA, Diaz OA, Grunwald S. 2009. Long-term water quality trends after implementing best management practices in South Florida. Journal of Environmental Quality 38: 1683–1693. Diaz OA, Anderson DL, Hanlon EA. 1993. Phosphorus mineralization from histosols of the Everglades Agricultural Area. Soil Science 156(3): 178–185. Duan Q, Gupta VK, Sorooshian S. 1992. Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resources Research 28: 1015–1031. Eckhardt K, Arnold JG. 2001. Automatic calibration of a distributed catchment model. Journal of Hydrology 251: 103-109. Fensel D. 2003. Ontologies: a Silver Bullet for Knowledge Management and Electronic Commerce. Springer-Verlag: Berlin. Franchini M, Galeati G, Berra S. 1998. Global optimization techniques for the calibration of conceptual rainfall-runoff models. Hydrological Sciences Journal 43: 443–458. Garcia R, Izuno F, Scarlatos P. 2001. Water tables and drainage uniformity in the Everglades agricultural area. ASAE Meeting Paper no. 01-2299. American Society of Agricultural Engineers: St Joseph, MI. Goldberg DE. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley: Reading, Mass. Green WH, Ampt GA. 1911. Studies on soil physics: 1. Flow of air and water through soils. The Journal of Agricultural Science 4: 1–24. Grieb TM, Hudson RJM, Shang N, Spear RC, Gherini SA, Goldstein RA. 1999. Examination of model uncertainty and parameter interaction in a global carbon cycling model (GLOCO). Environment International 25: 787–803. Gruber TR. 1993. A translation approach to portable ontology specifications. Knowledge Acquisition 5(2): 199–220. Gupta VK, Sorooshian S. 1985. The relationship between data and the precision of parameter estimates of hydrologic-models. Journal of Hydrology 81(1–2): 57–77. Hogue TS, Sorooshian S, Gupta H, Holz A, Braatz D. 2000. A Multi-step automatic calibration scheme for river forecasting models. Journal of Hydrometeorology 1: 524–542. Hooghoudt SB. 1940. General Consideration of the Problem of Field Drainage by Parallel Drains, Ditches, Watercourses, and Channels. Bodemkundig Instituut: Groningen, The Netherlands. Inmanbamber NG. 1994. Temperature and seasonal effects on canopy development and light interception of sugarcane. Field Crops Research 36(1): 41–51. Kwon HY, Grunwald S, Beck HW, Jung Y, Daroub SH, Lang TA, Morgan KT. 2010. Ontology-based simulation of water flow in organic soils applied to Florida sugarcane. Agricultural Water Management 97(1): 112–122. Madramootoo CA, Johnston WR, Ayars JE, Evans RO, Fausey NR. 2007. Agricultural drainage management, quality and disposal issues in North America. Irrigation and Drainage 56: S35–S45. Madsen H. 2003. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Advances in Water Resources 26(2): 205–216. Irrig. and Drain. 63: 538–549 (2014)
SIMULATING WATER TABLE FLUCTUATIONS ON FARMS IN SOUTH FLORIDA
Mrochzkovski M, Raper GP, Kuczena G. 1997. The quest for more powerful validation of conceptual catchment models. Water Resources Research 33: 2325–2336. Nangia V, Gowda PH, Mulla DJ, Sands GR. 2010. Modeling Impacts of tile drain spacing and depth on nitrate-nitrogen losses. Vadose Zone Journal 9(1): 61–72. Nelder JA, Mead R. 1965. A simplex-method for function minimization. Computer Journal 7(4): 308–313. Parton WJ, Stewart JWB, Cole CV. 1998. Dynamics of C, N, P, and S in grassland soils: a model. Biogeochemistry 5: 109–131. Reyes MR, Bengtson RL, Fouss JL, Rogers JS. 1993. Gleams hydrology submodel modified for shallow-water table conditions. Transactions of the ASAE 36 (6): 1771–1778. Sharma V, Swayne DA, Lam D, Schertzer W. 2006. Parallel shuffled complex evolution algorithm for calibration of hydrological models. Paper read at the 20th International Symposium on High Performance Computing Symposium, at St John’s, Canada, 14-17 May. Sharpley AN, Jones CA, Gray C, Cole CV. 1984. A simplified soil and plant phosphorus model. 2. Prediction of labile, organic, and sorbed phosphorus. Soil Science Society of America Journal 48(4): 805–809.
Copyright © 2014 John Wiley & Sons, Ltd.
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Skaggs RW. 1980. DRAINMOD Reference Report: Methods for Design and Evaluation of Drainage Water Management Systems for Soils with High Water Tables. North Carolina State University: Raleigh. Skaggs RW, Breve MA, Gilliam JW. 1994. Hydrologic and water-quality impacts of agricultural drainage. Critical Reviews in Environmental Science and Technology 24(1): 1–32. Sogbedji JM, McIsaac GF. 2006. Evaluation of the ADAPT model for simulating nitrogen dynamics in a tile-drained agricultural watershed in central Illinois. Journal of Environmental Quality 35(5): 1914–1923. Solomatine DP, Dibike YB, Kukuric N. 1999. Automatic calibration of groundwater models using global optimization techniques. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques 44 (6):879–894. Studer R, Benjamins VR, Fensel D. 1998. Knowledge engineering: principles and methods. Data & Knowledge Engineering 25(1–2): 161–197. Van Genuchten MT. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44: 892–898. Vrugt JA, Gupta HV, Bouten W, Sorooshian S. 2003. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research 39(8): 1201–1217.
Irrig. and Drain. 63: 538–549 (2014)
