Search Advanced

AIMS Agriculture and Food

2016, Volume 1, Issue 2: 208-226. doi: 10.3934/agrfood.2016.2.208
Previous Article Next Article
Research article

Development and Use of Mathematical Models and Software Frameworks for Integrated Analysis of Agricultural Systems and Associated Water Use Impacts

  • 1.
    Dept. of Mathematics, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
  • 2.
    Dept. of Mathematical Sciences, Clemson University, O105 Martin Hall, Clemson, SC 29634, USA
  • 3.
    Dept. of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
  • 4.
    Dept. of Mathematics, Indiana University of Pennsylvania, 210 South 10th St., Indiana, PA 15705 USA
  • 5.
    California Water Science Center, U.S. Geological Survey, 4165 Spruance Road, San Diego, CA 92140, USA
  • Received: 31 January 2016 Accepted: 18 May 2016 Published: 25 January 2016

  • The development of appropriate water management strategies requires, in part, a methodology for quantifying and evaluating the impact of water policy decisions on regional stakeholders. In this work, we describe the framework we are developing to enhance the body of resources available to policy makers, farmers, and other community members in their e orts to understand, quantify, and assess the often competing objectives water consumers have with respect to usage. The foundation for the framework is the construction of a simulation-based optimization software tool using two existing software packages. In particular, we couple a robust optimization software suite (DAKOTA) with the USGS MF-OWHM water management simulation tool to provide a flexible software environment that will enable the evaluation of one or multiple (possibly competing) user-defined (or stakeholder) objectives. We introduce the individual software components and outline the communication strategy we defined for the coupled development. We present numerical results for case studies related to crop portfolio management with several defined objectives. The objectives are not optimally satisfied for any single user class, demonstrating the capability of the software tool to aid in the evaluation of a variety of competing interests.

    Citation: K.R. Fowler, E.W. Jenkins, M. Parno, J.C. Chrispell, A.I. Colón, R.T. Hanson. Development and Use of Mathematical Models and Software Frameworks for Integrated Analysis of Agricultural Systems and Associated Water Use Impacts[J]. AIMS Agriculture and Food, 2016, 1(2): 208-226. doi: 10.3934/agrfood.2016.2.208

    Related Papers:

  • The development of appropriate water management strategies requires, in part, a methodology for quantifying and evaluating the impact of water policy decisions on regional stakeholders. In this work, we describe the framework we are developing to enhance the body of resources available to policy makers, farmers, and other community members in their e orts to understand, quantify, and assess the often competing objectives water consumers have with respect to usage. The foundation for the framework is the construction of a simulation-based optimization software tool using two existing software packages. In particular, we couple a robust optimization software suite (DAKOTA) with the USGS MF-OWHM water management simulation tool to provide a flexible software environment that will enable the evaluation of one or multiple (possibly competing) user-defined (or stakeholder) objectives. We introduce the individual software components and outline the communication strategy we defined for the coupled development. We present numerical results for case studies related to crop portfolio management with several defined objectives. The objectives are not optimally satisfied for any single user class, demonstrating the capability of the software tool to aid in the evaluation of a variety of competing interests.


    加载中
    [1] M. Maupin, J. Kenny, S. Hutson, J. Lovelace, N. Barber and K. Linsey, (2014), Estimated use of water in the United States in 2010, Circular 1405, U.S. Geological Survey, Available from: http://dx.doi.org/10.3133/cir1405.
    [2] I. James and S. Reilly, (2015), Pumped beyond limits, many U.S. aquifers in decline, The Desert Sun.
    [3] J. Thomas, G. Stanton, J. Bumgarner, D. Pearson, A. Teeple, N. Houston, J. Payne and M. Musgrove, (2013), A conceptual hydrogeologic model for the hydrogeologic framework, geochemistry, and groundwater-flow system of the Edwards-Trinity and related aquifers in the Pecos County region, Texas, Fact Sheet 2013-3024, U.S. Geological Survey.
    [4] F. Morris, (2013), Western Kansas farmers face dwindling water supply, Available from: http: //hereandnow.wbur.org/2013/08/19/kansas-farmers-water.
    [5] D. Steward, P. Bruss, X. Yang, S. Staggenborg, S. Welch and M. Apley, (2013), Tapping unsustainable groundwater stores for agricultural production in the High Plains aquifer of Kansas, projections to 2110, Proceedings of the National Academy of Sciences, 110: E3477–E3486.
    [6] J. Peterson and Y. Ding, (2004), Economic adjustments to groundwater depletion in the High Plains: Do water-saving irrigation systems save water?, Am. J. Agr. Econ., 87: 147–159.
    [7] V. McGuire, M. Johnson, R. Schie er, J. Stanton, S. Sebree and I. Verstraeten, (2002), Water in storage and groundwater management approaches, High Plains Aquifer, 2000, Circular, U.S. Geological Survey.
    [8] J. Musick, F. Pringle,W. Harman and B. Stewart, (1990), Long-term irrigation trends – Texas High Plains, Appl. Eng. Agric., 6: 717–724.
    [9] M. Sophocleous, (2010), Review: groundwater management practices, challenges, and innovations in the High Plains Aquifer, USA – lessons and recommended actions, Hydrogeol. J., 18: 559–575.
    [10] California Water Science Center, (2016), The California drought, Available from: http://ca. water.usgs.gov/data/drought/drought-impact.html.
    [11] V. McGuire, (2014), Water-level changes and change in water in storage in the High Plains Aquifer, Predevelopment to 2013 and 2011–2013, Scientific Investigations Report 2014–5218, U.S. Geological Survey.
    [12] B. Scanlon, C. Faunt, L. Longuevergne, R. Reedy, W. Alley, V. McGuire and P. McMahon, (2012), Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley, PNAS, 109: 9320–9325.
    [13] J. Medina, (2015), California cuts farmers’ share of scant water, The New York Times, Available from: http://www.nytimes.com/2015/06/13/us/ california-announces-restrictions-on-water-use-by-farmers.html?_r=0.
    [14] D. Charles, (2013), Kansas farmers commit to taking less water from the ground, All Things Considered, Available from: http://www.npr.org/blogs/thesalt/2013/10/22/230702453/ in-kansas.
    [15] H. Wells, (2015), Vegetables and pulses, Available from: http:www.ers.usda.gov/topics/ crops/vegetables-pulses.aspx.
    [16] D. Goolsby, (2015), Coachella Valley agriculture industry continues growing, The Desert Sun.
    [17] H. Cooley, K. Connelly, R. Phurisamban and M. Subramanian, (2015), Impacts of California’s Ongoing Drought: Agriculture, Technical report, Pacific Institute, Oakland, CA, Available from: http://pacinst.org/publication/ impacts-of-californias-ongoing-drought-agriculture/.
    [18] W. Gomaa, N. Harraz and A. el Tawil, (2011), Crop planning and water management: A survey, in Proceedings of the 41st International Conference on Computers & Industrial Engineering, Los Angeles, CA, 319–324.
    [19] J. Dury, N. Schaller, F. Garcia, A. Reynaud and J. Bergez, (2012), Models to support cropping plan and crop rotation decisions: a review, Agronomy Sust. Developm., 32: 567–580.
    [20] P. deVoil, W. Rossing and G. Hammer, (2006), Exploring profit – sustainability trade-o s in cropping systems using evolutionary algorithms, Environ. Modell. Softw., 21: 1368–1374.
    [21] J. E. Annetts and E. Audsley, (2002), Multiple objective linear programming for environmental farm planning, J Oper. Res. Soc., 53: 933–943.
    [22] R. Beneke and R. Winterboer, (1984), Linear Programming. Applications to Agriculture, Aedos.
    [23] J. Groot, G. Oomen and W. Rossing, (2012), Multi-objective optimization and design of farming systems, Agr. Syst., 110: 63–77.
    [24] B. Sahoo, A. Lohani and R. Sahu, (2006), Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning, Water Resour. Manag., 20: 931–948.
    [25] R. Sarker and T. Ray, (2009), An improved evolutionary algorithm for solving multi-objective crop planning models, Comput. Electron. Agric., 68: 191–199.
    [26] R. Hanson, S. Boyce,W. Schmid, J. Hughes, S. Mehl, S. Leake, T. Maddock III and R. Niswonger, (2014), One-Water Hydrologic Flow Model (MODFLOW–OWHM), Techniques and Methods 6- A51, U.S. Geological Survey, Available from: http://dx.doi.org/10.3133/tm6A51.
    [27] W. Schmid and R. Hanson, (2009), The Farm Process Version 2 (FMP2) for MODFLOW-2005 — Modifications and Upgrades to FMP1, Techniques in Water Resources Investigations 6-A32, U.S. Geological Survey.
    [28] C. Faunt, R. Hanson, K. Belitz and L. Rogers, (2009), California’s Central Valley Groundwater Study: A powerful new tool to assess water resources in California’s Central Valley, Fact Sheet 2009-3057, U.S. Geological Survey.
    [29] C. Faunt, C. Stamos, L. Flint, M. Wright, M. Burgess, M. Sneed, J. Brandt, A. Coes and P. Martin, (2015), Hydrogeology, hydrologic e ect of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California, Scientific Investigations Report 2015–5150, U.S. Geological Survey.
    [30] R. Hanson,W. Schmid, J. Lear and C. Faunt, (2008), Simulation of an aquifer-storage-and-recovery (ASR) system for agricultural water supply using the farm process in MODFLOW for the Pajaro Valley, Monterey Bay, California, in MODFLOW and More 2008: Groundwater and Public Policy, International Ground Water Modeling Center, Colorado School of Mines, 501–505.
    [31] R. Hanson, W. Schmid, J. Knight and T. Maddock III, (2013), Integrated hydrologic modeling of a transboundary aquifer system - Lower Rio Grande, in MODFLOW and More 2013: Translating Science into Practice, International Ground Water Modeling Center, Colorado School of Mines, Golden, CO, 5 p.
    [32] R. Hanson,W. Schmid, C. Faunt and B. Lockwood, (2010), Simulation and analysis of conjunctive use with MODFLOW’s farm process, Ground Water, 48: 674–689.
    [33] W. Schmid, J. P. King and T. Maddock III, (2009), Conjunctive Surface-water/groundwater Model in the Southern Rincon Valley Using MODFLOW-2005 with the Farm Process, Technical report, New Mexico Water Resources Research Institute.
    [34] R. Hanson, W. Schmid, C. Faunt, J. Lear and B. Lockwood, (2014), Integrated hydrologic model of Pajaro Valley, Santa Cruz and Monterery Counties, California, Scientific Investigations Report 2014-5111, U.S. Geological Survey.
    [35] R. Hanson, L. Flint, A. Flint, M. Dettinger, C. Faunt, D. Cayan and W. Schmid, (2012), A method for physically based model analysis of conjunctive use in response to potential climate changes, Water Resour. Res., 48: 23.
    [36] B. Adams, L. Bauman, W. Bohnho , K. Dalbey, M. Ebeida, J. Eddy, M. Eldred, P. Hough, K. Hu, J. Jakeman, L. Swiler and D. Vigil, (2009), DAKOTA: A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 5.4 User’s Manual, Technical report SAND2010-2183, Sandia National Laboratories (updated April 2013).
    [37] K. Fowler, E. Jenkins, C. Ostrove, J. Chrispell, M. Farthing and M. Parno, (2014), A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-o s in crop selection, Environ. Modell. Softw., 69: 280–291.
    [38] N. Lehmann, R. Finger, T. Klein, P. Calanca and A. Walter, (2013), Adapting crop management practices to climate change: Modeling optimal solutions at the field scale, Agr. Syst., 117: 55–65.
    [39] S. Lautenbach, M. Volk, M. Stauch and G. Whittaker, (2013), Optimization-based trade-o analysis of biodiesel crop production for managing agricultural catchment, Environ. Modell. Softw., 48: 98– 112.
    [40] J. Bokhiria, K. Fowler and E. Jenkins, (2014), Modelling and optimization for crop portfolio management under limited irrigation strategies, Journal of Agriculture and Environmental Sciences, 2: 1–13.
    [41] P. Steduto, T. Hsiao, E. Fereres and D. Raes, (2012), Crop Yield Response to Water, FAO irrigation and drainage, Paper 66, Food and Agriculture Organization of the United Nations.
    [42] A. Harbaugh, (2005), MODFLOW-2005: The U.S. Geological Survey modular ground-water model: The groundwater flow process, Techniques and Methods 6-A16, U.S. Geological Survey.
    [43] W. Schmid, R. Hanson, T. M. III and S. Leake, (2006), User guide for the farm process (FMP1) for the U.S. Geological Survey’s modular three-dimensional finite-di erence ground-water flow model, MODFLOW-2000, Techniques and Methods 6-A17, U.S. Geological Survey.
    [44] R. Hanson and W. Schmid, (2013), Economic Resilience through “One-Water” Management, Open File Report 2013-1175, U. S. Geological Survey.
    [45] W. Schmid, R. Hanson, S. Leake, J. Hughes and R. Niswonger, (2014), Feedback of land subsidence on the movement and conjunctive use of water resources, Environ. Modell. Softw., 62: 253–270.
    [46] I. Ferguson and D. Llewellyn, (2015), Simulation of Rio Grande project operations in the Rincon and Mesilla Basins: Summary of model configuration and results, Technical Memorandum 86– 68210–2015–05, U.S. Bureau of Reclamation.
    [47] G. Schoups, C. Addams, J. Minjares and S. Gorelick, (2006), Sustainable conjunctive water management in irrigated agriculture: Model formulation and application to the Yaqui Valley, Mexico, Water Resour. Res., 42: 19.
    [48] W. Schmid and R. Hanson, (2007), Simulation of intra- or trans-boundary water-rights hierarchies using the farm process for MODFLOW-2000, J. Water Res. Pl. – ASCE, 133: 166–178.
    [49] S. Boyce and R. Hanson, (2015), An integrated approach to conjunctive–use analysis with the one– water hydrologic flow model, MODFLOW-OWHM, in MODFLOW and More 2015: Modeling a Complex World – Integrated Modeling to Understand and Manage Water Supply, Water Quality, and Ecology, Colorado School of Mines, Golden, CO, 6–10.
    [50] S. Boyce, (2015), Model Reduction via Proper Orthogonal Decomposition of Transient Confined and Unconfined Groundwater Flow, PhD thesis, University of California Los Angeles.
    [51] S. Boyce, T. Nishikawa and W. Yeh, (2015), Reduced order modeling of the Newton formulation of MODFLOW to solve unconfined groundwater flow, Adv. Water Res., 83: 250–262.
    [52] C. Faunt, (2009), Groundwater availability of the Central Valley aquifer, California, Professional Paper 1766, U. S. Geological Survey.
    [53] R. Hanson, B. Lockwood and W. Schmid, (2014), Analysis of projected water availability with current basin management plan, Pajaro Valley, California, J. Hydrol., 519: 131–147.
    [54] R. Hanson, L. Flint, C. Faunt, D. Gibbs and W. Schmid, (2014), Hydrologic models and analysis of water availability in Cuyama Valley, California, Science Investigations Report 2014-5150, U.S. Geological Survey, Available from: http://dx.doi.org/10.3133/sir20145150.
    [55] R. Hanson and D. Sweetkind, (2014), Water availability in Cuyama Valley, California, Fact Sheet FS2014-3075, U.S. Geological Survey.
    [56] T. Russo, A. Fisher and B. Lockwood, (2014), Assessment of managed aquifer recharge site suitability using a GIS and modeling, Ground Water, 53: 1–12.
    [57] H. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L. Matott, M. Cunha, G. Dandy, M. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D. Solomatine, J. Vrugt, A. Zecchin, B. Minsker, E. Barbour, G. Kuczera and F. Pasha, (2014), Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions, Environ. Modell. Softw., 62: 271–299.
    [58] J. Eddy and K. Lewis, (2001), E ective generation of Pareto sets using genetic programming, in Proceedings DETC ’01: ASME 2001 Design Engineering Technical Conferences, Pittsburgh, PA, 1–9.
    [59] K. Demchak, J. Harper and L. Klime, Strawberry production, Available from: http://extension.psu.edu/business/ag-alternatives/horticulture/fruits/ strawberry-production#section-4.
    [60] R. Smith, A. Baameur, M. Bari, M. Cahn, D. Giraud, E. Natwick and E. Takele, Artichoke production in California, Available from: http://anrcatalog.ucanr.edu/pdf/7221.pdf.
    [61] M. LeStrange, M. Cahn, S. Koike, R. Smith, O. Daugovish, S. Fennimore, E. Natwick, S. Dara, E. Takele and M. Cantwell, Artichoke production in California, Available from: http://anrcatalog.ucanr.edu/pdf/7211.pdf.
  • Reader Comments
  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
AIMS Agriculture and Food
1.9 3.7

Metrics

Article views(8310) PDF downloads(1977) Cited by(2)

Preview PDF
Download XML
Export Citation
Article outline

Figures and Tables

Figures(6)  /  Tables(3)

Other Articles By Authors

Related pages

Tools

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog